ALBERT

Description: This paper presents a method to estimate steady wind and a maneuvering strategy for loitering under a strong, steady wind. The wind estimation uses Global Positioning System velocity only through a novel filter design without airspeed measurement. The wind estimate is then used to guide the aircraft to crab in a direction perpendicular to the wind, thereby avoiding large changes in flight variables if an orbit-type maneuver is attempted. The proposed method is demonstrated through flight tests.

Description: We propose biased estimators to find the direction of arrival of emitters present in the mainlobe of a spinning antenna-based electronic intelligence system. The proposed estimators were constructed by using Bayesian techniques and by performing a linear transformation and an affine transformation on the maximum likelihood estimator. From a Monte Carlo simulation and experimental results, we demonstrate that the proposed estimators outperform the limit set by the popular performance benchmark, the Cramer-Rao lower bound.

Description: A new method was proposed for chirp signal detection and estimation built on the frame-based fast Fourier transform (FFT). The proposed method uses the peak frequency difference between FFT frames to detect a chirp signal and estimate chirp rate. This approach differs from conventional methods and is easy to implement. It generates more accurate chirp rate estimation especially under a low signal-to-noise ratio. Simulation and experimental data are used to verify the proposed methods.

Description: The quasimaximum likelihood (QML) estimator of polynomial-phase signals (PPSs) is based on the maximization of the short-time Fourier transform and suffers from aliasing when signals are sampled below the Nyquist sampling rate. In this paper, a phase unwrapping procedure has been proposed as an additional step in the QML to estimate parameters of such signals. Statistical study has shown excellent performance of the proposed approach.

Description: GaN p-i-n ultraviolet avalanche photodiodes (UV-APDs) were fabricated from epitaxial structures grown on low-dislocation-density free-standing GaN substrates to form $4times 4$ UV-APD arrays with a device size of $75times 75~mu text{m}^{{ {2}}}$ . The devices in the UV-APD array showed a uniform and reliable distribution of breakdown voltage ( $V_{text {BR}}$ ) and leakage current density. The average $V_{text {BR}}$ of the 16 devices in one of the UV-APD arrays was 96±0.6 V, and the average dark current density ( $J_{R_{}{text {Dark}}}$ ) and photocurrent density ( $J_{R_{}{text {Photo}}}$ ) were measured to be (6.5±1.8) $times 10^{-7}$ and (5.7±1.1) $times 10^{{text {-6}}}$ A/cm $^{{{2}}}$ at the reverse bias voltage of $V_{R}=48$ V (50% of the average onset point of $V_{text {BR}}$ ), respectively. The reliable device performance was confirmed by performing multiple reverse bias $I$ – $V$ scans for the selected devices in the UV-APD array. We also observed the significantly enhanced spectral resp- nsivity from the 142 to 5485 mA/W due to the strong carrier impact ionization at high reverse bias.

Description: An optimized broadband source emitting from 1064 to 1600 nm was specially designed for coherent anti-Stokes Raman scattering spectroscopy. This source is based on the use of a ytterbium-doped photonic crystal fiber with a large core in which a supercontinuum is generated from a signal wave at 1064 nm regenerated by ytterbium ions pumping. A particularly flat spectrum with high spectral power density and perfectly synchronized spectral components is obtained.

Description: We experimentally demonstrate switchable dual-wavelength mode-locking of thulium-doped fiber laser (TDFL), using single-wall carbon nanotubes as saturable absorber. Due to the cavity birefringence-induced comb filter, switchable mode-locking can be individually realized for the proposed TDFL among three wavelengths of 1947, 1945, and 1943 nm, with almost the same 3-dB spectral bandwidth of 2.2 nm, repetition rate of 13.6 MHz, and pulsewidth of 1.8 ps. Furthermore, after finely adjusting the intra-cavity birefringence, we are able to demonstrate switchable dual-wavelength mode-locking at either 1947/1945 or 1945/1943 nm. The optical spectra of dual-wavelength mode-locking have almost the same characteristics and can maintain stable operation for a long period.

Description: We report high-responsivity GaN/InGaN heterojunction phototransistors (HPTs) grown on sapphire substrates. Under the ultraviolet (UV) photon illumination from the front side of the wafer, an HPT shows broad photoresponse spectrum with the short-wavelength cutoff wavelength well beyond $lambda = 280$ nm, and the UV-to-visible-band rejection ratio is $> 8 times 10^{3}$ . The responsivity ( $R_{lambda }$ ) of HPT is greater than 8 A/W at $lambda = 373$ nm, and is greater than 3 A/W at $lambda = 280$ nm as the device is biased at $V_{mathrm{ CE}}=10$ V. As the HPT is biased at the near breakdown voltage ( $V_{mathrm{ CE}}> 35$ V), the responsivity performance was enhanced due to the carrier multiplication, resulting in $R_{lambda }> 100$ A/W at $V_{mathrm{ CE}}= 40$ V for $P_{mathrm{ opt}}=1.73~mu text{W}$ /cm 2 at $lambda = 373$ nm. These results demonstrate that GaN/InGaN HPTs can achieve low light detection with a broadband photon response in the near-UV-to-deep-UV spectral ranges.

Description: The multiplexing and the amplification of 2- $mu text{m}$ vortex beams are experimentally verified in a Ho:YAG crystal rod amplifier. Spatially multiplexed vortex beams are studied and the amplification results are presented and discussed. The integrity of the launched vortex beams is well maintained through the amplification process. Further discussions are provided to increase the gain as well as for power scaling. Due to the nature of Ho:YAG material and the geometry of the rod amplifier, our system shows the potential of working as a power amplifier for vortex beams.

Description: Gallium-nitride (GaN)-based light-emitting diodes (LEDs) are highly efficient sources for general purpose illumination. Visible light communications (VLC) uses these sources to supplement existing wireless communications by offering a large, licence-free region of optical spectrum. Here, we report on progress in the development of micro-scale GaN LEDs (micro-LEDs), optimized for VLC. These blue-emitting micro-LEDs are shown to have very high electrical-to-optical modulation bandwidths, exceeding 800 MHz. The data transmission capabilities of the micro-LEDs are illustrated by demonstrations using ON–OFF-keying, pulse-amplitude modulation, and orthogonal frequency division multiplexing modulation schemes to transmit data over free space at the rates of 1.7, 3.4, and 5 Gb/s, respectively.

Description: Multicarrier waveforms bring several major advantages over single carrier waveforms in radar systems: frequency diversity, waveform diversity, short time on target and the possibility to optimize the transmitted waveforms, to mention a few. Interesting waveform designs utilizing diversity have been proposed in the literature already. In this paper, we develop a generalized model that can accommodate a wide variety of design options, both existing and novel ones, in an easy and intuitive way. The developed matrix equations for transmitter and receiver allow for implementing different waveforms simply by filling in the elements to corresponding matrices accordingly. Moreover, intuitive agile generation of waveforms in simulation environments and in practice is facilitated. Waveform optimization examples are provided using the derived model. A Mutual Information based criterion is employed to formulate the optimization problems which are solved analytically. Novel multicarrier spread spectrum waveforms are proposed and generated using the derived model. The radar performance of one of these waveforms is investigated through simulations. It is demonstrated that it can outperform well-known existing multicarrier waveforms. It is also shown that such waveform can lower the peak-to-average-power ratio due to the spreading operation, which is a benefit for the front end designs of the transmitter and receiver.

Description: In this paper, the secrecy performance of finite-sized cooperative cyclic prefixed single carrier systems with multiple eavesdroppers and unreliable wireless backhaul connections across multiple transmitters is investigated. For nonidentical frequency-selective fading channels between the relay and destination nodes, secrecy performance metrics including the secrecy outage probability, ergodic secrecy rate, and probability of nonzero achievable secrecy rate are derived. Furthermore, the existence of performance limits on the secrecy outage probability and probability of non-zero achievable secrecy rate are verified for various backhaul scenarios. These limits are found to be exclusively determined by the backhaul reliability. For imperfect backhaul connections, it is found that the diversity gain promised by cooperative cyclic prefixed single-carrier systems cannot be achieved in the conventional asymptotic high signal-to-noise ratio region. Link-level simulations are conducted to verify the derived impact of backhaul reliability on the secrecy performance.

Description: Recently, several high-resolution parameter estimation algorithms have been developed to exploit the structure of strictly second-order (SO) non-circular (NC) signals. They achieve a higher estimation accuracy and can resolve up to twice as many signal sources compared to the traditional methods for arbitrary signals. As a benchmark for these NC methods, we derive the closed-form deterministic $R$ -D NC Cramér-Rao bound (NC CRB) for the multi-dimensional parameter estimation of strictly non-circular (rectilinear) signal sources in this paper. Assuming a separable centro-symmetric $R$ -D array, we show that in some special cases, the deterministic $R$ -D NC CRB reduces to the existing deterministic $R$ -D CRB for arbitrary signals. This suggests that no gain from strictly non-circular sources (NC gain) can be achieved under the deterministic data assumption in these cases. For more general scenarios, finding an analytical expression of the NC gain for an arbitrary number of sources is very challenging. Thus, in this paper, we simplify the derived NC CRB and the existing CRB for the special case of two closely-spaced strictly non-circular sources captured by a uniform linear array (ULA). Subsequently, we use these simplified CRB expressions to analytically compute the maximum achievable asymptotic NC gain for the considered two source case. The resulting expression only depends on the various physical parameters and we find the conditions that provide the largest NC gain. Our analysis is supported by extensive simulation results.

Description: As communication systems scale up in bandwidth, the limited resolution in high-speed analog-to-digital converters (ADCs) is a key challenge in realizing low-cost “mostly digital” transceiver architectures. This motivates a systematic effort to understand the limits of such architectures under the severe quantization constraints imposed by the use of low-precision ADCs. In particular, we investigate a canonical problem of blind carrier phase and frequency synchronization with coarse phase quantization in this paper. We develop a Bayesian approach to blind phase estimation, jointly modeling the unknown data, unknown phase and the quantization nonlinearity. We highlight the crucial role of dither, implemented via a mixed signal architecture with a digitally controlled phase shift prior to the ADC. We show the efficacy of random dither, and then improve upon its performance with a simple feedback control policy that is close to optimal in terms of rapidly reducing the mean squared error of phase estimation. This initial blind phase acquisition stage is followed by feedback-based phase/frequency tracking using an Extended Kalman Filter. Performance evaluations for a QPSK system show that excellent bit error rate (BER) performance, close to that of an unquantized system, is achieved by the use of 8 phase bins (implementable using 4 one-bit ADCs operating on linear combinations of in-phase and quadrature components).

Description: State estimation is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structured observation process wherein a subset of states emit distinct flags with some probability while other states are unmeasured. For flag HMMs, an explicit computation of the probability of error for the maximum-likelihood filter and smoother is developed. Also, the form of the optimal filter is further characterized in terms of the time since the last flag, and this result is used to further simplify the error-probability computation. Some preliminary graph-theoretic insights into the error probability and its computation are discussed. Finally, these algebraic and structural results are leveraged to address sensor placement in two examples, including one on activity-monitoring in a home environment that is drawn from field data. These examples indicate that low error-probability filtering and smoothing can be achieved with relatively few sensors.

Description: In this paper, we consider the problem of multi-parameter estimation in the presence of compound Gaussian clutter for cognitive radar by the variational Bayesian method. The advantage of variational Bayesian is that the estimation of multi-variate parameters is decomposed to problems of estimation of univariate parameters by variational approximation, thus enabling analytically tractable approximate posterior densities in complex statistical models consisting of observed data, unknown parameters, and hidden variables. We derive the asymptotic Bayesian Cramer–Rao bounds and demonstrate by numerical simulations that the proposed approach leads to improved estimation accuracy than the expectation maximization method and the exact Bayesian method in the case of non-Gaussian nonlinear signal models and small data sample size.

Description: Spatiotemporal signal reconstruction from samples randomly gathered in a multidimensional space with uncertainty is a crucial problem for a variety of applications. Such a problem generalizes the reconstruction of a deterministic signal and that of a stationary random process in one dimension, which was first addressed by Whittaker, Kotelnikov, and Shannon. In this work we analyze multidimensional random sampling with uncertainties jointly accounting for signal properties (signal spectrum and spatial correlation) and for sampling properties (inhomogeneous sample spatial distribution, sample availability, and non-ideal knowledge of sample positions). The reconstructed signal spectrum and the signal reconstruction accuracy are derived as a function of signal and sampling properties. It is shown that some of these properties expand the signal spectrum while others modify the spectrum without expansion. The signal reconstruction accuracy is first determined in a general case and then specialized for cases of practical interests. The optimal interpolator function is derived and asymptotic results are obtained to show the impact of sampling non-idealities. The analysis is corroborated by verifying that previously known results can be obtained as special cases of the general one and by means of a case study accounting for various settings of signal and sample properties.

Description: We study strategies for enhanced secrecy using cooperative jamming in secure communication systems with limited rate feedback. A Gaussian multiple-input multiple-output (MIMO) wiretap channel with a jamming helper is considered. The transmitter and helper both require channel state information (CSI), which is quantized at the receiver and fed back through two sum-rate-limited feedback channels. The quantization errors result in reduced beamforming gain from the transmitter, as well as interference leakage from the helper. First, under the assumption that the eavesdropper's CSI is completely unknown, we derive a lower bound on the average main channel rate and find the feedback bit allocation that maximizes the jamming power under a constraint on the bound. For the case where statistical CSI for the eavesdropper's channel is available, we derive a lower bound on the average secrecy rate, and we optimize the bound to find a suitable bit allocation and the transmit powers allocated to the transmitter and helper. For the case where the transmitter and helper have the same number of antennas, we obtain a closed-form solution for the optimal bit allocation. Simulations verify the theoretical analysis and demonstrate the significant performance gain that results with intelligent feedback bit allocation and power control.

Description: As a good way to represent target backscatter measured by high-frequency synthetic aperture radar (SAR) systems, the attributed scattering center (ASC) model is able to provide concise and physically relevant features of a complex target and has played an important role in model-based automatic target recognition (ATR). However, most existing ASC feature extraction methods suffer from imprecise image segmentation or high computational cost, which greatly encumber their practical applications. To tackle this problem, we present a novel ASC feature extraction algorithm for SAR targets based on Lévy random fields in a nonparametric Bayesian framework. Specifically, Lévy random fields, yielding a natural sparse representation of the unknown ASC model, are introduced to construct prior distributions, which lead to the specification of a joint prior distribution for the number of ASCs and the ASC associated parameters. Meanwhile, the problem may be formulated as a sparse representation problem, with regularization induced through the Lévy random field prior. We also develop a reversible jump Markov chain Monte Carlo (RJ-MCMC) method to enable relatively fast posterior inference. Experimental results confirm the effectiveness and efficiency of the proposed algorithm.

Description: The optical and electrical properties of organic solar cells with metallic Ag incorporating into the PEDOT:PSS layer in the case of oblique incidence are numerically investigated based on in house finite-difference frequency-domain code. It is found that the light absorption decreases with the increase of incident angle, and the absorption enhancement varies slightly due to the contribution of the broadband surface plasmon (SP)-mode. Since the absorption reduces at large incident angle, the short-circuit current undoubtedly decreases, thus fill factor and power conversion efficiency reduce accordingly. However, there is little variety of open-circuit voltage, which indicates that open-circuit voltage is independent of incident angle. Our results can be used as a reference for better understanding of the optical and electrical properties of organic solar cells with oblique incidence.

Description: Through co-design of a dual SiGe transimpedance amplifier and an integrated silicon photonic circuit, we realized for the first time an ultra-compact and low-power silicon single-polarization coherent receiver operating at 40 GBd. A bit-error rate of $〈3.8times 10^{{{-3}}}$ was obtained for an optical signal-to-noise ratio of 14 dB for QPSK modulation (80 Gb/s), and 26.5 dB for 16-QAM (160 Gb/s). We also demonstrate robust performance of the receiver over temperature and wavelength.

Description: The future technology migration in access networks compels the development of key innovative transmitters operating at 10 Gb/s around 1550 nm and capable of transmitting data in extended reach passive optical networks (>60 km). A laser modulated directly appears to be a low cost and a simple solution to address these needs. However, the extinction ratio and the distance of transmission are limited by frequency chirping inherent to high bit rate modulation at 1550 nm. In this letter, we demonstrate the monolithic integration of a directly modulated lasers and a ring resonator (RR) and show the possibility to engineer the RR with specific frequency modulation efficiency. The ring is used as an optical eye reshaper, which enables the increase of the extinction ratio above 9 dB. Transmissions up to 65 km with 5.8 dBm of modulated optical power coupled into the fiber are demonstrated, proving the feasibility of our transmitter for the next generation passive optical network stage 2.

Description: We propose an imaging receiver (ImR) using angle diversity detectors for indoor space division multiplexing-based visible light communication (SDM-VLC) systems. Compared with a conventional ImR which utilizes vertically oriented detectors, the proposed imaging angle diversity receiver (ImADR) enjoys two main advantages, including a wider field-of-view (FOV) and higher optical gain. Both theoretical analysis and Monte Carlo simulations are carried out to evaluate the performance of the proposed ImADR in an indoor four-channel SDM-VLC system. Analytical results show that, for a target bit error rate of $10^{mathrm { {-3}}}$ , the proposed ImADR-based SDM-VLC system achieves 44% reduction in the transmit optical power and 130% coverage improvement than the system using a conventional ImR.

Description: Compared with Mach–Zehnder modulators, silicon microring modulators have the important advantages of smaller footprint and significantly lower energy per bit but suffer the disadvantages of inherent wavelength dependence and a modulation bandwidth that is limited by the resonator bandwidth. Here, we address the latter disadvantage of using an integrated silicon microring modulator with spectrally efficient modulation with 16-quadrature amplitude modulation on direct detection orthogonal frequency division multiplexing (OFDM). We demonstrate a line rate of 128-Gb/s and a net data rate of 107.6-Gb/s OFDM signal modulation using this tunable microring modulator. The 6.6- and 10-km single-mode fiber transmission of the modulated signal is presented. The measured bit error rates of back-to-back, 6.6-km, and 10-km fiber transmission are well below the forward error correction threshold.

Description: We report on the experimental fabrication and functioning of an optical fiber sensing probe operating on the principle of a localized surface plasmon resonance (LSPR) at near infrared (NIR) wavelengths, around the 2- $mu text{m}$ window. As indium–tin oxide exhibits an absorption peak at NIR wavelengths, we have synthesized and characterized ITO nanoparticles with a doping concentration of 9:1 of In:Sn and attached these onto the unclad core of the fiber. Corresponding to the absorption peak of the bare nanoparticles, the fiber probe displays an LSPR peak at 1950 nm, which falls in the signature band of many bacterial contaminants. The probe is further used for the sensing of analytes of refractive indices varying from 1.335 to 1.375. The results reveal that the average peak absorbance of the probe changes by 3.46 absorbance units for a unit change in refractive index.

Description: Photonic band crystal semiconductor lasers generating gain-switched picosecond pulses in the 1060-nm spectral range are demonstrated. The unique ultra-broad laser waveguide allows the simultaneous delivery of high output power and high beam quality, enabling the generation of optical pulses with peak brightness up to 130 MWcm $^{-2}$ sr $^{-1}$ . The spectral dynamics are investigated by external filtering of the laser emission. The effects of the filter bandwidth and central wavelength on the resulting optical pulse characteristics are identified. By properly chosen filter parameters, it is found that the higher order relaxation oscillation part of the optical pulse is completely suppressed even at the highest injected pulse currents, also resulting in a pulse width reduction down to less than 21.5 ps.

Description: We propose and demonstrate a polarization beam splitter (PBS) based on a tapered directional coupler (DC). The tapered DC structure has the advantage of being insensitive to variations of the coupling length and the local coupling coefficient, and thus can significantly increase the bandwidth and polarization extinction ratio (ER). The fabricated PBS with one waveguide width tapered from 0.55 to 0.41 $mu text{m}$ and another tapered reversely in a length of 29 $mu text{m}$ shows a polarization ER above 16 dB and an insertion loss less than 0.4 dB over a wavelength range of 100 nm. The fabrication tolerance of the proposed PBS is also analyzed to be ±50 nm, ensuring the compatibility with the standard 180-nm complementary metal–oxide–semiconductor process.

Description: Gain enhancement of single-mode Cr-doped crystalline core fiber (SMCDCCF) with longer fiber length fabricated by online laser-heated pedestal growth (LHPG) technique is demonstrated. In comparison with the SMCDCCF fabricated without online growth, the online technique enables real-time monitoring and controlling small molten zone in the LHPG to achieve longer length with better uniformity and smaller core diameter of the SMCDCCFs. The SMCDCCF exhibits a length of 10.6 cm, a core diameter of 25 $mu text{m}$ , and a $V$ -value of 2.40, which confirms the LP 01 single-mode operation by the far-field pattern measurement. A 3.9-dB gross gain and a 1.9-dB net gain of the SMCDCCF at the wavelength of 1400 nm were obtained. The gross and net gains are the highest yet reported of the SMCDCCFs. Further development on higher gain of the SMCDCCF may be functioned the SMCDCCF as a broadband fiber amplifier for use in the next-generation fiber transmission systems.

Description: This letter focuses on a spatial modulation (SM)-based multiple input single output optical wireless communication system. In the system, both input-dependent noise and finite alphabet are employed. Based on information theory, the theoretical expressions of the mutual information and its lower bound are derived. By maximizing the minimum distance, an optimization problem for precoding design is formulated. Then, an interior point method-based iteration algorithm is proposed to solve the problem. Numerical results show that the performance of the proposed SM scheme outperforms that of the conventional, choosing the best-gain channel scheme. Moreover, the proposed precoding scheme can provide better system performance than the scheme without precoding.

Description: A Bayesian framework is proposed to define flexible coupling models for joint tensor decompositions of multiple datasets. Under this framework, a natural formulation of the data fusion problem is to cast it in terms of a joint maximum a posteriori (MAP) estimator. Data-driven scenarios of joint posterior distributions are provided, including general Gaussian priors and non Gaussian coupling priors. We present and discuss implementation issues of algorithms used to obtain the joint MAP estimator. We also show how this framework can be adapted to tackle the problem of joint decompositions of large datasets. In the case of a conditional Gaussian coupling with a linear transformation, we give theoretical bounds on the data fusion performance using the Bayesian Cramér–Rao bound. Simulations are reported for hybrid coupling models ranging from simple additive Gaussian models to Gamma-type models with positive variables and to the coupling of data sets which are inherently of different size due to different resolution of the measurement devices.

Description: The objective of generalized sampling expansion (GSE) is the reconstruction of an unknown, continuously defined function $fleft(tright)$ from samples of the responses from $M$ linear time-invariant (LTI) systems that are each sampled using the $1/M$ th Nyquist rate. In this paper, we investigate the GSE for lowpass and bandpass signals with multiple sampling rates in the fractional Fourier transform (FRFT) domain. First, we propose an improvement of Papoulis’ GSE, which has multiple sampling rates in the FRFT domain. Based on the proposed GSE, we derive the periodic nonuniform sampling scheme and the derivative interpolation method by designing different fractional filters and selecting specific sampling rates. In addition, the Papoulis GSE and the previous GSE associated with FRFT are shown to be special instances of our results. Second, we address the problem of the GSE of fractional bandpass signals. A new GSE for fractional bandpass signals with equal sampling rates is derived. We show that the restriction of an even number of channels in the GSE for fractional bandpass signals is unnecessary, and perfect signal reconstruction is possible for any arbitrary number of channels. Further, we develop the GSE for a fractional bandpass signal with multiple sampling rates. Lastly, we discuss the application of the proposed method in the context of single-image super-resolution reconstruction based on GSE. Illustrations and simulations are presented to verify the validity and effectiveness of the proposed results.

Description: We consider a sensor scheduling problem where the sensors have multiple choices of communication channel to send their local measurements to a remote state estimator for state estimation. Specifically, the sensors can transmit high-precision data packets over an expensive channel or low-precision data packets, which are quantized in several bits, over some cheap channels. The expensive channel, though being able to deliver more accurate data which leads to good estimation quality at the remote estimator, can only be used scarcely due to its high cost (e.g., high energy consumption). On the other hand, the cheap channel, though having a small cost, delivers less accurate data which inevitably deteriorates the remote estimation quality. In this work we propose a new framework in which the sensors switch between the two channels to achieve a better tradeoff among the communication cost, the estimation performance and the computational complexity, where the two-channel case can be easily extended to a multiple-channel case. We propose an opportunistic sensor schedule which reduces the communication cost by randomly switching among the expensive and cheap channels, and in the meantime maintains low computational complexity while introducing data quantization into the estimation problem. We present a minimum mean square error (MMSE) estimator in a closed-form under the proposed opportunistic sensor schedule. We also formulate an optimization problem to search the best opportunistic schedule with a linear quantizer. Furthermore, we show that the MMSE estimator in the limiting case becomes the standard Kalman filter.

Description: The stability problems of the least mean fourth (LMF) algorithm put a limitation on its tracking capability. The paper investigates the possibility of solving this problem via stabilization of the algorithm. The analysis is done for a Markov plant. It is found that the available stable normalized LMF (NLMF) algorithm has a tracking limitation for high signal-to-noise ratio. The paper presents a new stable NLMF algorithm that is free of this limitation. Mean-square stability of the algorithm is proved. Expressions are derived for the minimum steady-state mean square deviation (MSD) and the corresponding convergence time. The new algorithm outperforms the available stable NLMF algorithm in both the transient and steady states. The new algorithm is also compared with the NLMS algorithm when the adaptation parameter of each algorithm is set to the value that minimizes its steady-state MSD. For large initial MSD, the algorithm outperforms the NLMS algorithm, even for Gaussian noise. For small initial MSD, the algorithm outperforms the NLMS algorithm for sub-Gaussian noise, while the situation is opposite for Gaussian noise. Analytical results are supported by simulations.

Description: Twisted particle filters are a class of sequential Monte Carlo methods recently introduced by Whiteley and Lee to improve the efficiency of marginal likelihood estimation in state-space models. The purpose of this article is to extend the twisted particle filtering methodology, establish accessible theoretical results which convey its rationale, and provide a demonstration of its practical performance within particle Markov chain Monte Carlo for estimating static model parameters. We derive twisted particle filters that incorporate systematic or multinomial resampling and information from historical particle states, and a transparent proof which identifies the optimal algorithm for marginal likelihood estimation. We demonstrate how to approximate the optimal algorithm for nonlinear state-space models with Gaussian noise and we apply such approximations to two examples: a range and bearing tracking problem and an indoor positioning problem with Bluetooth signal strength measurements. We demonstrate improvements over standard algorithms in terms of variance of marginal likelihood estimates and Markov chain autocorrelation for given CPU time, and improved tracking performance using estimated parameters.

Description: This paper deals with a novel generalization of classical blind source separation (BSS) in two directions. First, relaxing the constraint that the latent sources must be statistically independent. This generalization is well-known and sometimes termed independent subspace analysis (ISA). Second, jointly analyzing several ISA problems, where the link is due to statistical dependence among corresponding sources in different mixtures. When the data are one-dimensional, i.e., multiple classical BSS problems, this model, known as independent vector analysis (IVA), has already been studied. In this paper, we combine IVA with ISA and term this new model joint independent subspace analysis (JISA). We provide full performance analysis of JISA, including closed-form expressions for minimal mean square error (MSE), Fisher information and Cramér–Rao lower bound, in the separation of Gaussian data. The derived MSE applies also for non-Gaussian data, when only second-order statistics are used. We generalize previously known results on IVA, including its ability to uniquely resolve instantaneous mixtures of real Gaussian stationary data, and having the same arbitrary permutation at all mixtures. Numerical experiments validate our theoretical results and show the gain with respect to two competing approaches that either use a finer block partition or a different norm.

Description: This paper addresses the design of adaptive subspace matched filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signal-free observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar $rho $ . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high-dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus adaptive normalized matched filter (ANMF) detectors under mismatch scenarios.

Description: In this paper, a direct approach of designing robust weighted fusion steady-state Kalman predictors with uncertain noise variances is presented. Based on the steady-state Kalman filtering theory, using the minimax robust estimation principle, the local and six weighted fusion robust steady-state Kalman predictors are proposed based on the worst case systems with the conservative upper bounds of noise variances. They include the three robust weighted state fusers, two robust weighted measurement fusers, and a modified robust covariance intersection (CI) fuser. Their actual prediction error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. A Lyapunov equation approach for robustness analysis and the concept of the robust accuracy are presented and their robust accuracy relations are proved. A simulation example verifies the accuracy relations and robustness.

Description: In multitarget tracking, the problem of track labeling (assigning labels to tracks) is an ongoing research topic. The existing literature, however, lacks an appropriate measure of uncertainty related to the assigned labels that has a sound mathematical basis as well as clear practical meaning to the user. This is especially important in a situation where well separated targets move in close proximity with each other and thereafter separate again; in such a situation, it is well known that there will be confusion on target identities, also known as "mixed labeling." In this paper, we specify comprehensively the necessary assumptions for a Bayesian formulation of the multitarget tracking and labeling (MTTL) problem to be meaningful.We provide a mathematical characterization of the labeling uncertainties with clear physical interpretation.We also propose a novel labeling procedure that can be used in combination with any existing (unlabeled)MTT algorithm to obtain a Bayesian solution to the MTTL problem. One advantage of the resulting solution is that it readily provides the labeling uncertainty measures. Using the mixed labeling phenomenon in the presence of two targets as our test bed, we show with simulation results that an unlabeled multitarget sequential Monte Carlo (M-SMC) algorithm that employs sequential importance resampling (SIR) augmented with our labeling procedure performs much better than its "naive" extension, the labeled SIR M-SMC filter.

Description: Track-before-detect methods jointly detect and track one or several targets from raw sensor measurements. They often require the computation of the measurement likelihood conditional on the hidden state, which depends on the complex amplitudes of the targets. Since these amplitudes are unknown and fluctuate over time, this likelihood must be marginalized over the complex amplitude (i.e., phase and modulus). It has been demonstrated that this marginalization can be done analytically over the phase in the monotarget case. In this article, we first propose to extend the marginalization to the modulus in a monotarget setting, and we show that closed forms can be obtained for fluctuations of type Swerling 1 and 3. Second, we demonstrate that, in a multitarget setting, a closed form can be obtained for the Swerling 1 case. For Swerling 0 and 3 models, we propose some approximation to alleviate the computation. Since many articles consider the case of squared modulus measurements, we also consider this specific case in monoand multitarget settings with Swerling 0, 1, and 3 fluctuations. Finally, we compare the performance in estimation and detection for the different cases studied, and we show the gain, both in detection and estimation, of the complex measurement method over the squared modulus method, for any fluctuation model.

Description: When a bistatic inverse synthetic aperture radar (ISAR) system fails to collect complete radar cross section (RCS) datasets, bistatic ISAR (Bi-ISAR) images are usually corrupted using the conventional Fourier transform (FT)-based imaging algorithm. To overcome this problem, this paper proposes a new Bi-ISAR image reconstruction method that includes three steps: suboptimal estimation of parameters regarding the bistatic angle in the Bi-ISAR signal model via an orthogonal matching pursuit-type group-searching scheme, Bi-ISAR signal reconstruction using the estimated parameters, and Bi-ISAR image generation using the FT-based imaging algorithm applied to the reconstructed Bi-ISAR signal. To validate the reconstruction capability of the proposed method, bistatic-scattered field data using the physical optics technique as well as the point-scatterer model are used for Bi-ISAR image reconstruction. The results show that the proposed sparse-recovery-interpolation approach based on the Bi-ISAR signal model reconstruction combined with the classical FT-based algorithm can yield high reconstruction accuracy for incomplete bistatic RCS data compared to conventional numerical interpolation methods and existing direct sparse reconstruction techniques.

Description: Bistatic inverse synthetic aperture radar (ISAR) is an efficient geometric configuration of ISAR to image targets traveling along radar transmitter's line of sight, but it has the problem of defocusing and distortion because of the time-varying bistatic angle. In this paper, we derive the defocusing term and the distortion term by expanding the bistatic angle using first-order Taylor expansion. In addition, necessary constraints to neglect the defocusing term are provided via point spread function analysis, and a novel method to eliminate distortion based on linked scatterers is proposed. Simulation results validate our theoretical analysis and show the effectiveness of our method.

Description: Micro-Doppler (MD)-based target classification capabilities of automotive radars can provide high reliability and short latency to future active safety automotive features. A large number of pedestrians surrounding a vehicle in practical urban scenarios mandate prioritization of their treatment level. Distinguishing between relevant pedestrians that are crossing the street or are within the vehicle path and those that are on the sidewalks and are moving along the vehicle route can significantly minimize the number of vehicle-to-pedestrian accidents. This work proposes a novel technique for estimating a pedestrian direction of motion that treats pedestrians as complex distributed targets and utilizes their MD radar signatures. The MD signatures are shown to be indicative of pedestrian direction of motion, and a supervised regression is used to estimate the mapping between the directions of motion and the corresponding MD signatures. In order to achieve higher regression performance, a state-of-the-art sparse dictionary learning-based feature extraction algorithm was adopted from the field of computer vision by drawing a parallel between the Doppler effect and the video temporal gradient. The performance of the proposed approach is evaluated in practical automotive scenario simulations, where a walking pedestrian is observed by a multiple-input/multiple-output automotive radar with a two-dimensional rectangular array. The simulated data was generated using the statistical Boulic-Thalman human locomotion model. Accurate direction of motion estimation was achieved by using support vector regression and multilayer perceptron-based regression algorithms. The results show that the direction estimation error is less than 10° in 95% of the tested cases for pedestrian at a range of 100 m from the radar.

Description: This paper proposes a novel phase synchronization technique that enables beamforming with multiple resource-limited spacecraft in space and capitalizes on their spatial geometry. The proposed technique employs an external beacon to obviate the need for explicit time synchronization and reduces the accuracy requirements on localization. Results show that subcentimeter (subnanosecond)-level phase synchronization can be achieved with localization accuracy in the order of meters.

Description: The use of chirp signals in modern radar and ranging systems have numerous benefits. They are extensively used to improve signal-to-noise ratio and range resolution. The performance capabilities of these signals are directly related to their time-bandwidth product, i.e., the duration and bandwidth of the pulse. Ultra-wideband chirp signals are further desirable because they span a large bandwidth, making them resistant to narrowband environmental interference. The accurate detection and measurement of high chirp signals is difficult due to the necessity of a high-sampling analog-digital converter, a target measurement platform with high computational power, and a time-of-arrival (TOA) estimator with high temporal resolution. The difficulty of the problem is further compounded with the requirement that no a priori knowledge of the signal, noise, or operating environment is known. This paper presents a practical approach and implementation of a high linear chirp rate receiver and TOA estimator pair capable of detecting and measuring stationary radio frequency pulses as well as linear chirp rates up to 1.18 GHz in 400 ns. The high-resolution TOA algorithm and linear chirp receiver have been prototyped, synthesized, and placed and routed for a Virtex 6 SX475 FPGA.

Description: In this paper, an impact angle control guidance law, which considers simultaneously the impact angle and seeker's look angle constraints, is proposed for a constant speed missile against a stationary target. An optimal control theory with state variable inequality constraint is used to design the guidance law, for which a control energy performance index with the weighting function of the range-to-go is minimized. Various forms of guidance and trajectory shaping are possible by selecting a proper gain of the weighting function. To handle the seeker's look angle limits when the missile trajectory is highly curved by controlling the impact angle, the proposed guidance law generates three types of acceleration commands as the guidance phases: the first acceleration command for an initial guidance phase makes an initial seeker's look angle reach the maximum look angle; the second one for a midguidance phase maintains the maximum look angle; the final one for a terminal guidance phase intercepts the target with the desired impact angle. The performance of the proposed guidance law was investigated with nonlinear simulations for various engagement conditions.

Description: This paper presents a means of near asteroid hovering of a rigid spacecraft in the asteroid body-fixed frame with parameter variations and external disturbances. An adaptive finite-time control scheme is proposed, where the upper bounds of the parametric uncertainties and disturbances are not required for controller design. The detailed design principles and a rigorous stability analysis are provided. Finally, a body-fixed hovering maneuver is employed in numerical simulation to verify the effectiveness of the proposed strategy.

Description: In global navigation satellite system (GNSS) receivers, the acquisition process is the first stage of the signal processing module. It consists of assessing the presence of GNSS signals and providing a rough estimation of the incoming signal parameters: the Doppler frequency and the code delay. However, the presence of bit sign transitions affects receiver performance in signal acquisition detection. This article focuses on the bit transition and its impact on the acquisition performance by providing a general mathematical study and an illustration for two GNSS signals: the global positioning system legacy civil signal (GPS L1 C/A) and Galileo E1 open service (OS). This study is applicable to a terrestrial user in a constraint environment. Furthermore, the presented results are mathematical models of the probability of detection in the presence of bit sign transitions (only one potential bit sign transition per integration interval), and potential uncertainties on the Doppler frequency and code delay. These do not result from empirical acquisition of real signals.

Description: This paper investigates the problem of ground vehicle tracking with ground-moving target indicator (GMTI) radar. In practice, the movement of ground vehicles may involve several different maneuvering types (acceleration, deceleration, standstill, etc.). Consequently, the GMTI radar may lose measurements when the radial velocity of the ground vehicle is below a threshold, i.e., falling into the Doppler blind region. In this paper, to incorporate the information gathered from normal measurements and knowledge on the Doppler blindness constraint, we develop an enhanced particle filtering method for which the importance distributions are inspired by a recent noise-related Doppler blind (NRDB) filtering algorithm for GMTI tracking. Specifically, when constructing the importance distributions, the proposed particle filter takes the advantages of the efficient NRDB algorithm by applying the extended Kalman filter and its generalization for interval-censored measurements. In addition, the linearization and Gaussian approximations in the NRDB algorithm are corrected by the weighting process of the developed filtering method to achieve a more accurate GMTI tracking performance. The simulation results show that the proposed method substantially outperforms the existing methods for the GMTI tracking problem.

Description: Sets that are defined either from closed contours or from a set of points are generic descriptors for several kinds of objects in binary images. In this paper, we derive a novel model in the space of sets and design an observer for the proposed model to estimate the depth and orientation of planar objects from a camera. This problem is well known as "structure from motion." When the objects are only partially projected on the image plane of the camera, our model makes object depth and orientation estimation possible without feature tracking and matching between distinct image frames (i.e., the so-called correspondence problem), which is an advantage over the image moments-based model. However, the proposed model fails in some situations (the failures can be seen as brief instabilities), and it is not always continuous. To compensate for these drawbacks, we designed a fast observer based on L 1 control theory with a binary signal for the proposed model. Stability analysis with respect to a certain asymptotic instability ratio is also presented in this paper. The effectiveness of the proposed model and observer is demonstrated through simulations and experimental results.

Description: Presents the front cover/table of contents for this issue of this publication.

Description: A novel differential vector phase locked loop (DVPLL) is derived that takes global navigation satellite system (GNSS) code-phase and carrier-phase measurements from a base station and uses them to maintain a position with centimeter-level accuracy directly in the vector tracking loop of a rover receiver. The navigation filter uses five state variables, three position and two clock states, to create the replica code and carrier measurements for a static test. There are no individual channel states or feedback mechanisms. Closing the individual loops solely through the navigation filter makes this a purely vector method. For short baselines, where differential atmospheric errors are small, the DVPLL can be used on single-frequency data. An L1-only live-sky static test was performed using the method, resulting in a three-dimensional accuracy of 1.3 mm for a zero-baseline and 5.3 mm for an 18.5 m baseline.

Description: An adaptive actuator failure and disturbance compensation scheme is developed for attitude tracking control of spacecraft. The proposed scheme consists of a composite parameter adaptation design that incorporates an adaptive backstepping feedback control law and an adaptive feedforward actuator failure compensator; it can guarantee the overall closed-loop system stability and asymptotic tracking. Illustrative simulation results of an application to a spacecraft model show that the designed actuator failure compensation controller ensures system stability and tracking performance.

Description: Track-before-detect (TBD) is a popular incoherent energy integration technique aimed at improving detectability of weak targets. A number of studies are available in the literature demonstrating its efficacy against disturbance (whether noise or clutter), but most of them refer to synthetic data, i.e., relying on computer simulations. In this paper, we tackle the problem of assessing the TBD performance with real data and in a particularly severe clutter environment, i.e., sea-clutter. Precisely, using a set of real data from a ground-based sea-search radar, we implement TBD directly on the plot-lists coming from the radar plot extractor (this be can done with acceptable complexity by using an ad hoc dynamic programming algorithm), and demonstrate its effectiveness in reducing sea-clutter. As a further contribution, we also develop an improved decision logic for plot confirmation.

Description: The concept of more electric aircraft leads to increases in the amount of electrical loads, as well as the power consumed in the aircraft of the future. To utilize the power feeders, more symmetric, load balancing methods can be applied to swap loads between different phases of an alternating current (AC) feeder, or even between different power feeders. If the load balancing system reacts to measurement data during the flight in real time, the cable power losses and return network power losses, too, are reduced. In addition, the rate of power management interventions decreases. The load balancing problem in three-phase systems is a mixed-integer nonlinear nondifferentiable optimization problem, which is typically solved by elaborate and time-consuming nonreal-time optimization algorithms. The AC loads in aircraft have different power factors, which result in currents described by complex numbers. To determine a load swapping scheme in real time starting from a given load allocation with sparse swapping, new heuristics are presented. One heuristic is specially designed to solve the phase swapping problem by shifting single-phase loads between phases of a feeder. Another heuristic, based on the first one, is enhanced to more than one three-phase feeder and considers the swapping of single-phase and three-phase loads. The heuristics are tested by simulations of a comprehensive case study based on real measurement data from a modern passenger aircraft. To prove the efficiency of the new concepts, a test bench has been built, and several experiments successfully conducted.

Description: The probability hypothesis density (PHD) filter is an efficient algorithm for multitarget tracking in the presence of nonlinearities and/or non-Gaussian noise. The sequential Monte Carlo (SMC) and Gaussian mixture (GM) techniques are commonly used to implement the PHD filter. Recently, a new implementation of the PHD filter using B-splines with the capability to model any arbitrary density functions using only a few knots was proposed. The spline PHD (SPHD) filter was found to be more robust than the SMC-PHD filter because it does not suffer from degeneracy, and it was better than the GM-PHD implementation in terms of estimation accuracy, albeit with a higher computational complexity. In this paper, we propose a multiple model extension to the SPHD filter to track multiple maneuvering targets. Simulation results are presented to demonstrate the effectiveness of the new filter.

Description: This paper proposes an optimal control framework for the climb and descent economy modes of a flight management system (FMS) yielding a solution that can be implemented in real-time flights below the drag divergence Mach number. The problem is formulated as the optimization of a functional that trades off the fuel- and time-related costs of a flight as a function of a (crew-supplied) parameter called the cost index. The work builds on previous research of the authors for the cruise phase and extends it to the climb and descent phases of flight. More specifically, for both climb and descent, it is found that suboptimal solutions can be obtained as the positive real roots of a fifth-degree polynomial lying inside the flight envelope, which can be found using fast-converging algorithms such as Newton's method. The main contributions of this work are threefold. First, the proposed method gives physical insight because there is an analytical expression for each coefficient of the polynomial. Second, this approach eliminates the need to have a performance database in the system, thus making its implementation faster in real-time. Third, the solution exhibits the same behavior of airborne FMS units as a function of the cost index, which is justified in this paper based on Bellman's principle of optimality. This justification is an important theoretical contribution of the paper. A validation of the approximate solution is obtained using the shooting method to compute the optimal trajectories and compare them against the proposed suboptimal solution. Simulation results show that, for an Airbus A320 model and for a Gulfstream-IV aircraft model, the relative error of the suboptimal trajectories when compared to the optimal trajectories is small for climb and descent trajectories, respectively.

Description: An X-band, 15-W-class gallium nitride (GaN) solid-state power amplifier (SSPA) for the 50-kg-class ultrasmall deep-space probe PROCYON has been demonstrated in deep space for the first time. PROCYON was launched on December 3, 2014, as a subpayload of the Hayabusa 2 spacecraft. The GaN SSPA consists of three-stage, single-end amplifiers. The final-stage GaN high-power amplifier, which dominates the characteristics of the SSPA, achieves a maximum drain efficiency of 55.8% at 8.4 GHz. The fabricated SSPA using this GaN high-power amplifier has dimensions of 150 x 120 x 62 mm and weighs 1.5 kg, and its space applicability was confirmed through space environmental tests. For more than 2450 h of continuous operation in deep space, the GaN SSPA has achieved an average output power of 41.7 dBm (14.8 W), with standard deviation of 0.12 dB, maximum overall efficiency of 35.7%, and average efficiency of 33.8%. To the authors' knowledge, this is the highest efficiency of all proven X-band onboard SSPAs.

Description: In this paper, an adaptive cubature Kalman filter (ACKF) is proposed to improve the performance of the conventional cubature Kalman filter. The ACKF uses a new cubature rule that combines a third-degree spherical rule with an adaptive higher degree radial rule along the directions of larger uncertainty. More accurate and robust results can be obtained with slightly more cubature points than the conventional third-degree cubature Kalman filter (CKF). Compared with other high-degree Gaussian filters, ACKF uses much fewer points but maintains very close performance. A target tracking problem is used to demonstrate the effectiveness of the proposed filter.

Description: We propose a simple method for estimating crystal oscillator g-sensitivity in inertially aided Global Navigation Satellite System (GNSS) receivers. It does not require any specific equipment, like GNSS signal simulators or rate tables. The method is based on analyzing closed-loop phase tracking errors. This enables us to utilize the actual GNSS signal as the frequency reference, despite the presence of an unknown Doppler shift in it. The method has been successfully applied to the calibration of an oven-controlled crystal oscillator.

Description: A robust minimax test for two composite hypotheses, which are determined by the neighborhoods of two nominal distributions with respect to a set of distances—called $alpha $ —divergence distances, is proposed. Sion's minimax theorem is adopted to characterize the saddle value condition. Least favorable distributions, the robust decision rule and the robust likelihood ratio test are derived. If the nominal probability distributions satisfy a symmetry condition, the design procedure is shown to be simplified considerably. The parameters controlling the degree of robustness are bounded from above and the bounds are shown to be resulting from a solution of a set of equations. The simulations performed evaluate and exemplify the theoretical derivations.

Description: This paper considers robust low-rank matrix completion in the presence of outliers. The objective is to recover a low-rank data matrix from a small number of noisy observations. We exploit the bilinear factorization formulation and develop a novel algorithm fully utilizing parallel computing resources. Our main contributions are i) providing two smooth loss functions that promote robustness against two types of outliers, namely, dense outliers drawn from some elliptical distribution and sparse spike-like outliers with small additive Gaussian noise; and ii) an efficient algorithm with provable convergence to a stationary solution based on a parallel update scheme. Numerical results show that the proposed algorithm obtains a better solution with faster convergence speed than the benchmark algorithms in both synthetic and real data scenarios.

Description: Subspace tracking using low-complexity methods is a common research area in signal processing. While numerical properties of available methods are extensively addressed in literature, there is a paucity of statistical results on limiting behavior of subspace tracking methods. In this contribution, we will study steady-state error of DPM, MALASE, Champagne's PA, Karasalo, and PAST methods. We show that DPM, MALASE, and PAST are approximations of the PA method, while Karasalo's method is equivalent to the PA method in convergence region. A tradeoff between steady-state error of methods and their local convergence rate is demonstrated. A nonstationary signal model is assumed, in which true subspace is slowly and randomly varying. Simulation results show PA and PAST are asymptotically close to the optimum sample covariance matrix (SCM), while this is not true for DPM and MALASE in stationary signal case. In the steady state, these methods can be expressed with a unified approach using stochastic first-order difference equations, whose fixed points are calculated. The results can be used to determine optimal step-size in terms of tolerable total signal subspace projection error. Simulation results confirm the validity of theoretical error calculations.

Description: In this paper, we propose a strategy that is based on expectation maximization for tracking multiple point targets. The algorithm is similar to probabilistic multi-hypothesis tracking (PMHT) but does not relax the point target model assumptions. According to the point target models, a target can generate at most one measurement, and a measurement is generated by at most one target. With this model assumption, we show that the proposed algorithm can be implemented as iterations of Rauch-Tung-Striebel (RTS) smoothing for state estimation, and the loopy belief propagation method for marginal data association probabilities calculation. Using example illustrations with tracks, we compare the proposed algorithm with PMHT and joint probabilistic data association (JPDA) and show that PMHT and JPDA exhibit coalescence when there are closely moving targets whereas the proposed algorithm does not. Furthermore, extensive simulations c comparing the mean optimal subpattern assignment (MOSPA) performance of the algorithm for different scenarios averaged over several Monte Carlo iterations show that the proposed algorithm performs better than JPDA and PMHT. We also compare it to benchmarking algorithm: $N$ -scan pruning based track-oriented multiple hypothesis tracking (TOMHT). The proposed algorithm shows a good tradeoff between computational complexity and the MOSPA performance.

Description: We consider the problem of low-rank decomposition of incomplete tensors. Since many real-world data lie on an intrinsically low-dimensional subspace, tensor low-rank decomposition with missing entries has applications in many data analysis problems such as recommender systems and image inpainting. In this paper, we focus on Tucker decomposition which represents an $N$ th-order tensor in terms of $N$ factor matrices and a core tensor via multilinear operations. To exploit the underlying multilinear low-rank structure in high-dimensional datasets, we propose a group-based log-sum penalty functional to place structural sparsity over the core tensor, which leads to a compact representation with smallest core tensor. The proposed method is developed by iteratively minimizing a surrogate function that majorizes the original objective function. This iterative optimization leads to an iteratively reweighted least squares algorithm. In addition, to reduce the computational complexity, an over-relaxed monotone fast iterative shrinkage-thresholding technique is adapted and embedded in the iterative reweighted process. The proposed method is able to determine the model complexity (i.e., multilinear rank) in an automatic way. Simulation results show that the proposed algorithm offers competitive performance compared with other existing algorithms.

Description: A kind of intensity-interrogated magnetic field sensor composed of ferrofluid and photonic crystal fiber (PCF) with a lateral-offset fusion splicing is proposed and investigated. The sensing principles are mainly based on the tunable absorption coefficient of ferrofluid and mode interference in the PCF section. Theoretical analyses show that the sensitivity depends on the allocation condition of the light intensity of the core and cladding modes in the PCF, which in turn depends on the amount of the lateral offset between the lead-in fiber and the PCF. Several sensors with different offset amounts are fabricated and tested. The experimental results show that a lateral-offset fusion splicing can significantly enhance the sensitivity of the sensor. In addition, the sensitivity as high as −0.0303 dB/Oe in the range of 20–160 Oe is achieved when setting the offset as 20 $mu text{m}$ .

Description: In this letter, we report a fiber optic biosensor based on surface plasmon resonance (SPR) for the detection of uric acid in aqueous samples. The sensing probe is prepared by successive coatings of silver and silicon layers over a small unclad length of the plastic clad silica optical fiber in the middle. Thereafter, the probe is dipped in a polyacrylamide gel containing enzyme uricase to immobilize it using a gel entrapment method. The sensor is characterized using a wavelength interrogation method. The SPR spectra corresponding to different concentrations of uric acid in the range of 0–0.9 mM are recorded. A red shift in the resonance wavelength is observed with an increase in the concentration of uric acid around the probe. In addition, the sensitivity of the proposed biosensor is studied in relation to parameters, such as concentration and pH of the uric acid solution. Finally, the sensor is highly selective and possesses better limit of detection and limit of quantification.

Description: We investigated all-optical shift register operations using 1.55- $mu text{m}$ polarization bistable vertical-cavity surface-emitting lasers (VCSELs) formed in an array. The VCSEL array consisted of VCSELs that oscillated with single longitudinal and transverse modes at room temperature. The lasing wavelengths were tuned by adjusting the driving currents of each VCSEL. A VCSEL in the array exhibited polarization flip-flop operation by injection of the orthogonally polarized set–reset input pulses. By injection of the VCSEL output light into another VCSEL in the same array through a polarizer and an optical gate, the second VCSEL also exhibited polarization flip-flop operation according to the polarization state of the first VCSEL. The shift register operation has been successfully demonstrated using two adjacent VCSELs in the array. The optical power needed for polarization switching is usually lower than the VCSEL output power. Thus, this shift register operation was achieved without optical amplifiers.

Description: A novel bending vector sensor formed by splicing 2-core fiber (2CF) between two segments of multimode fibers (MMFs) was proposed and fabricated, in which the MMFs serve as the mode splitting and coupling. Moreover, the 2CF used in the experiment was designed and fabricated in our labs, and the external core with a larger diameter would be provided with a higher bending sensitivity. Besides, the bending responses of the sensor are directionally sensitive due to the asymmetric structure of the 2CF. As a result, along with the implementation of the conformation experiments on the bending and temperature sensor property, and a good agreement has been achieved. The proposed sensor possesses many advantages, such as being inexpensive, compact, and robust.

Description: In this letter, we analyze three different carrier phase estimation approaches for coherent optical systems based on multi-subcarrier modulation, comparing them in terms of both performance and complexity. Averaging the estimated values on the subcarriers (SCs) significantly increases the laser linewidth tolerance at the expense of additional complexity, while using a single SC for carrier phase estimation yields a complexity reduction without any substantial performance loss with respect to performing a separate phase estimation on all SCs.

Description: In this letter, the clipping noise is reconstructed from the selected reliable observations in the frequency domain based on the modified compressed sensing (CS) algorithm for the asymmetrically clipped optical orthogonal frequency division multiplexing modulated visible light communication system. With the aid of the priori information obtained from the received time-domain signals, the proposed priori aided sparsity adaptive matching pursuit method improves the accuracy and robustness of the recovery performance. Simulation results show that the proposed scheme outperforms conventional CS-based clipping noise cancellation counterparts.

Description: The usual known methods for generating signals with given marginal probability and spectral properties cannot be applied to binary signals widely used in theoretical problems and communications systems. To overcome this difficulty, we first present some structural properties of power spectral densities, enabling the precise definition of the concept of spectral properties. This allows us to introduce a new method valid for symmetric binary random signals. This method uses some specific properties of filters with random impulse responses. Results of computer simulations show clearly the good performance of this method. Some extensions by using random thinning can further improve its performance.

Description: In this paper, we propose novel fractionally-spaced frequency-domain equalizers for the amplify-and-forward cooperative systems. In the proposed equalization schemes, the sampling rate of the received signal remains at least as high as the Nyquist rate within the digital processing chain of the relay node(s), upon which a true fractionally-spaced equalization becomes feasible at the destination. Based on minimum-mean-square-error (MMSE) criterion, different equalization structures (linear and non-linear) are designed, and approximations for their bit error rate (BER) performance are presented. The BER performance of the proposed schemes are further lower bounded through a matched-filter bound (MFB) analysis which provides insight into system design such as optimum power allocation and relay selection strategy. Our results show that, under certain channel realizations and sampling phase errors (that may occur in the relay and destination terminals), the performance of the conventional symbol-spaced cooperative systems reduces to that of no relay scenario. However, the performance of cooperative systems with the proposed fractionally-spaced equalizers is independent of the samplers' phases, and as a result, full benefit of cooperation is retained.

Description: We propose, test, and validate a novel Fourier-domain-based method for ghost image artifacts reduction in a common-path SSOCT system having multiple adjacent reference planes. Common-path probes with imaging systems containing high-index sapphire ball or other lenses produce multiple fixed references due to Fresnel reflections from the lens surfaces. The multiple reference planes produce multiple and overlapping optical coherence tomography (OCT) images. Since such ghost artifacts are the result of the superposition of multiple identical images having different amplitudes and spatial shifts, one can correctly shift and sum the images in the Fourier-domain once the relative amplitude and lateral position between the reference planes are known. This theory and numerical testing are presented to elucidate our method. We then validate the potential effectiveness using OCT imaging experiments.

Description: This paper computes the distortion power at the receiver side of an FBMC/OQAM-based OFDMA uplink channel under strong frequency selectivity and/or user timing errors. More precisely, it provides a distortion expression that is valid for a wide class of prototype pulses (not necessarily perfect-reconstruction ones) when the number of subcarriers is sufficiently large. This result is a valuable instrument for analyzing how users interfere to one another and to justify, formally, the common choice of placing an empty guard band between adjacent users. Interestingly, the number of out-band subcarriers contaminated by each user only depends on the prototype pulses and not on the channel nor on the equalizer. To conclude, the distortion analysis presented in this paper, together with some simulation results for a realistic scenario, also provide convincing evidence that FBMC/OQAM-based OFDMA is superior to classic circular-prefix OFDMA in the case of asynchronous users.

Description: The goal of an infection source node (e.g., a rumor or computer virus source) in a network is to spread its infection to as many nodes as possible, while remaining hidden from the network administrator. On the other hand, the network administrator aims to identify the source node based on knowledge of which nodes have been infected. We model the infection spreading and source identification problem as a strategic game, where the infection source and the network administrator are the two players. As the Jordan center estimator is a minimax source estimator that has been shown to be robust in recent works, we assume that the network administrator utilizes a source estimation strategy that can probe any nodes within a given radius of the Jordan center. Given any estimation strategy, we design a best-response infection strategy for the source. Given any infection strategy, we design a best-response estimation strategy for the network administrator. We derive conditions under which a Nash equilibrium of the strategic game exists. Simulations in both synthetic and real-world networks demonstrate that our proposed infection strategy infects more nodes while maintaining the same safety margin between the true source node and the Jordan center source estimator.

Description: Resampling is a standard step in particle filters and more generally sequential Monte Carlo methods. We present an algorithm, called chopthin, for resampling weighted particles. In contrast to standard resampling methods the algorithm does not produce a set of equally weighted particles; instead it merely enforces an upper bound on the ratio between the weights. Simulation studies show that the chopthin algorithm consistently outperforms standard resampling methods. The algorithms chops up particles with large weight and thins out particles with low weight, hence its name. It implicitly guarantees a lower bound on the effective sample size. The algorithm can be implemented efficiently, making it practically useful. We show that the expected computational effort is linear in the number of particles. Implementations for C++, R (on CRAN), Python and Matlab are available.

Description: In many application scenarios, the signals impinging on the array of sensors comprise the noncoherent (uncorrelated and/or partially correlated) signals and the coherent signals with several groups due to multipath propagation. In this paper, we consider the problem of estimating the number of noncoherent narrowband signals and that of coherent narrowband signals with multiple groups impinging on a planar sensor array composed of two parallel uniform linear arrays (ULAs), and an oblique projection based enumerator for the mixed signals (OPEMS) is proposed by utilizing the QR decomposition based ratio criterion (QRRC) for rank determination of a matrix. In the OPEMS, the number of noncoherent signals and that of coherent signals in each group are estimated separately, where only the elevation angles of noncoherent signals are estimated to isolate the coherent signals from the noncoherent ones, and the computationally intensive and time-consuming eigendecomposition procedure is avoided. The consistency of the proposed OPEMS is analyzed, and its effectiveness is verified through numerical examples.

Description: This paper considers the discrete sum rate maximization (DSRM) problem for beamformer optimization in multi-input single-output interference broadcast channels. In this problem, the achievable rates of the receivers are restricted to be taken from a set of finite discrete rate values, and such constraints arise from practical limitations with the modulation and coding schemes. Many existing studies consider sum rate maximization without the discrete rate constraints, and that may result in performance loss. In this paper, the DSRM problem is tackled via a convex approximation approach. Due to the discrete rate constraints, DSRM is a mixed-integer program. The idea of the proposed approach is to reformulate DSRM as a continuous, but still nonconvex, optimization problem. Then, appropriate convex approximations are applied. The advantage of the proposed approach is that the resulting approximate problems can be easily decomposed from a first-order optimization viewpoint. Utilizing this special feature, low-complexity and decentralized algorithms based on projected gradient are derived. Numerical results are used to show the efficiencies of the proposed algorithms in a multicell coordinated beamforming scenario. Also, this paper provides a proof for the convergence guarantee of one decentralized optimization strategy, namely, inexact maximum block improvement.

Description: This paper revisits grid based recursive approximate filtering of general Markov processes in discrete time, partially observed in conditionally Gaussian noise. The grid based filters considered rely on two types of state quantization, namely, the Markovian type and the marginal type. A set of novel, relaxed sufficient conditions is proposed, ensuring strong and fully characterized pathwise convergence of these filters to the respective MMSE state estimator. In particular, for marginal state quantizations, the notion of conditional regularity of stochastic kernels is introduced which, to the best of the authors' knowledge, constitutes the most relaxed condition under which asymptotic optimality of the respective grid based filters is guaranteed. Further, the convergence results are extended to include filtering of bounded and continuous functionals of the state, as well as recursive approximate state prediction. For both Markovian and marginal quantizations, the whole development of the respective grid-based filters relies more on linear-algebraic techniques and less on measure theoretic arguments, making the presentation considerably shorter and technically simpler.

Description: This paper addresses the issue of detecting change-points in time series. The proposed model, called the Bernoulli Detector, is presented first in a univariate context. This approach differs from existing counterparts by making only assumptions on the nature of the change-points, and does not depend on hypothesis on the distribution of the data, contrary to the parametric methods. It relies on the combination of a local robust statistical test, based on the computation of ranks and acting on individual time segments, with a global Bayesian framework able to optimize the change-points configurations from multiple local statistics, provided as $p$ -values. The control of the detection of a single change-point is proved even for small samples. The interest of such a generalizable nonparametric approach is shown on simulated data by the good performances attained for Gaussian noise as well as in presence of outliers, without adapting the model. The model is extended to the multivariate case by introducing the probabilities that the change-points affect simultaneously several time series. The method presents then the advantage to detect both unique and shared change-points for each signal. We finally illustrate our algorithm with real datasets from energy monitoring and genomic. Segmentations are compared to state-of-the-art approaches like the group lasso and the BARD algorithm.

Description: New schemes to recover signals defined in the nodes of a graph are proposed. Our focus is on reconstructing bandlimited graph signals, which are signals that admit a sparse representation in a frequency domain related to the structure of the graph. Most existing formulations focus on estimating an unknown graph signal by observing its value on a subset of nodes. By contrast, in this paper, we study the problem of inducing a known graph signal using as input a graph signal that is nonzero only for a small subset of nodes. The sparse signal is then percolated (interpolated) across the graph using a graph filter. Alternatively, one can interpret graph signals as network states and study graph-signal reconstruction as a network-control problem where the target class of states is represented by bandlimited signals. Three setups are investigated. In the first one, a single simultaneous injection takes place on several nodes in the graph. In the second one, successive value injections take place on a single node. The third one is a generalization where multiple nodes inject multiple signal values. For noiseless settings, conditions under which perfect reconstruction is feasible are given, and the corresponding schemes to recover the desired signal are specified. Scenarios leading to imperfect reconstruction, either due to insufficient or noisy signal value injections, are also analyzed. Moreover, connections with classical interpolation in the time domain are discussed. Specifically, for time-varying signals, where the ideal interpolator after uniform sampling is a (low-pass) filter, our proposed approach and the reconstruction of a sampled signal coincide. Nevertheless, for general graph signals, we show that these two approaches differ. The last part of the paper presents numerical experiments that illustrate the results developed through synthetic and real-world signal reconstruction problems.

Description: High SNR consistency of model order selection criteria in linear regression models has attracted a lot of attention in the signal processing community recently. It is now known that Exponentially Embedded Family, versions of Minimum Description Length etc. are high SNR consistent. However, a general framework for high SNR consistency in linear regression is still missing. This paper fills this gap by deriving necessary and sufficient conditions (NSCs) for model order selection criteria in both known and unknown noise variance situations to be high SNR consistent. Many popular model order selection criteria are proved to be high SNR consistent using these NSCs. We also provide a convergence rate analysis to discriminate between various high SNR consistent model order selection criteria. A direct application of model order selection techniques to the very important subset selection problem is computationally infeasible. This paper establish the high SNR consistency of an existing t-statistics based index ordering scheme that allows the conversion of a subset selection problem into a model order selection problem. Combining this index ordering with high SNR consistent model order selection criteria leads to a novel subset selection procedure (SSP) that is numerically shown to outperform existing high SNR consistent SSPs.

Description: In this paper, we study an optimal transmit strategy for multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. We study in detail the interesting case where the sum of the per-antenna power constraints is larger than sum power constraint. A closed-form characterization of an optimal beamforming strategy is derived. It is shown that we can always find an optimal beamforming transmit strategy that allocates the maximal sum power with phases matched to the complex channel coefficients. The main result is a simple recursive algorithm to compute the optimal power allocation. Whenever the optimal power allocation of the corresponding problem with sum power constraint only exceeds per-antenna power constraints, it is optimal to allocate maximal per-antenna power to those antennas to satisfy the per-antenna power constraints. The remaining power is divided amongst the other antennas whose optimal allocation follows from a reduced joint sum and per-antenna power constraints problem of smaller channel coefficient dimension and reduced sum power constraint. Finally, the theoretical results are illustrated by numerical examples.

Description: First, we study a basic kernel adaptive filtering algorithm based on stochastic restricted-gradient , which is a natural extension of the naive online regularized risk minimization algorithm (NORMA). Our error surface analysis shows that the algorithm has a decorrelation property due to its explicit use of kernel matrix. It turns out that its normalized version projects the current coefficient vector onto the same hyperplane, but with a different metric, as the kernel normalized least mean square (KNLMS) algorithm. This gives us a fundamental insight bridging two classes of kernel adaptive filtering algorithms. Second, we use this insight to derive an efficient forward-backward splitting algorithm. The same metric as used for the normalized algorithm is employed in the forward step, whereas the ordinary Euclidean metric is employed in the backward step, leading to efficient adaptive refinements of the filter dictionary. We show a monotone approximation property of the proposed algorithm with respect to some modified cost function under certain conditions. Numerical examples show the efficacy of the proposed algorithm.

Description: This paper presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer. First, we construct a general linear equation considered in large dimensions whose solution yields the steering vector mismatch. Then, we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. We also devise adaptive algorithms based on stochastic gradient (SG) and conjugate gradient (CG) techniques to update the beamforming weights with low complexity and avoid any costly matrix inversion. The main advantages of the proposed low-rank and mismatch estimation techniques are their cost-effectiveness when dealing with high-dimension subspaces or large sensor arrays. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the compared RAB methods.

Description: In this paper, we propose a novel cross-range scaling technique to estimate the rotational velocity (RV) of a maneuvering target. The proposed method includes three steps. First, a feature from accelerated segment test (FAST) is applied to two sequential inverse synthetic aperture radar (ISAR) images to find the locations of their robust feature points. Second, the rotation angle (RA) is estimated using two major axes, which are obtained using a principal component analysis (PCA) of the two feature data sets scaled by a candidate RV. Third, an RV search operation based on the measured RA is carried out via the bisection algorithm, which optimizes a newly devised cost function. Compared with the conventional method, the proposed method has two main advantages: 1) it requires no information about the rotation center of a target, and 2) it can efficiently generate a well-scaled ISAR image within a very short time. Finally, the results of experiments using point scatterers and real flying aircraft are provided to demonstrate the validity of the proposed method.

Description: The complexity of narrow transition band FIR filters is high and can be reduced by using frequency-response masking (FRM) techniques. These techniques use a combination of periodic model filters and, possibly periodic, masking filters. Time-multiplexing is in general beneficial since only rarely does the technology bound maximum obtainable clock frequency and the application determined required sample rate correspond. Therefore, architectures for time-multiplexed FRM filters that benefit from the inherent sparsity of the periodic filters are introduced in this paper. We show that FRM filters not only reduce the number of multipliers needed, but also have benefits in terms of memory usage. Despite the total amount of samples to be stored is larger for FRM, it results in fewer memory resources needed in FPGAs and more energy efficient memory schemes in ASICs. In total, the power consumption is significantly reduced compared with a single-stage implementation. Furthermore, we show that the choice of the interpolation factor that gives the least complexity for the periodic model filter and subsequent masking filter(s) is a function of the time-multiplexing factor, meaning that the minimum number of multipliers not always corresponds to the minimum number of multiplications. Both single-port and dual-port memories are considered, and the involved tradeoff in number of multipliers and memory complexity is illustrated. The results show that, for FPGA implementation, the power reduction ranges from 23% to 68% for the considered examples.

Description: The authors study online supervised learning under the empirical zero-one loss and introduce a novel classification algorithm with strong theoretical guarantees. The proposed method is a highly dynamical self-organizing decision tree structure, which adaptively partitions the feature space into small regions and combines (takes the union of) the local simple classification models specialized in those regions. The authors’ approach sequentially and directly minimizes the cumulative loss by jointly learning the optimal feature space partitioning and the corresponding individual partition-region classifiers. They mitigate overtraining issues by using basic linear classifiers at each region while providing a superior modeling power through hierarchical and data adaptive models. The computational complexity of the introduced algorithm scales linearly with the dimensionality of the feature space and the depth of the tree. Their algorithm can be applied to any streaming data without requiring a training phase or a priori information, hence processing data on-the-fly and then discarding it. Therefore, the introduced algorithm is especially suitable for the applications requiring sequential data processing at large scales/high rates. The authors present a comprehensive experimental study in stationary and nonstationary environments. In these experiments, their algorithm is compared with the state-of-the-art methods over the well-known benchmark datasets and shown to be computationally highly superior. The proposed algorithm significantly outperforms the competing methods in the stationary settings and demonstrates remarkable adaptation capabilities to nonstationarity in the presence of drifting concepts and abrupt/sudden concept changes.

Description: The authors consider the distributed estimation of a Gaussian vector with a linear observation model in an inhomogeneous wireless sensor network, in which a fusion center (FC) reconstructs the unknown vector using a linear estimator. Sensors employ uniform multi-bit quantizers and binary PSK modulation, and they communicate with the FC over orthogonal power- and bandwidth-constrained wireless channels. The authors study transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the mean-square error (MSE). In particular, they derive two closed-form upper bounds on the MSE in terms of the optimization parameters and propose “coupled” and “decoupled” resource allocation schemes that minimize these bounds. The authors show that the bounds are good approximations of the simulated MSE and that the performance of the proposed schemes approaches the clairvoyant centralized estimation when the total transmit power and bandwidth is very large. They investigate how the power and rate allocations are dependent on the sensors’ observation qualities and channel gains and on the total transmit power and bandwidth constraints. Their simulations corroborate their analytical results and demonstrate the superior performance of the proposed algorithms.

Description: We propose and demonstrate a new and effective framework to calibrate the scanning electron microscope (SEM), under variational magnifications from 20 to 500. Unlike previous approaches that required different models and calibration procedures for different magnifications, this framework regards the SEM as a black box. The general imaging model is first introduced in modeling the SEM system, to explore the nature of SEM imaging. In order to relax the complexity of the general imaging model and calibration process, the smooth general imaging model and a linear point-based calibration method are developed. The experimental results well agree with the theoretical predication and show great potential to realize SEM calibration.

Description: We propose and experimentally demonstrate a photonic generation scheme for multiband ultra-wideband (MB-UWB) signal by employing a dual-drive Mach–Zehnder modulator (DDMZM). In this scheme, four electrical binary pulse streams with different bit rates are combined to drive one radio frequency (RF) port of the DDMZM, while the other RF port is modulated by the sum of their 1-b delayed duplicates. By properly adjusting the bit rate and the amplitude of each pulse stream, as well as the bias voltage of the DDMZM, an MB-UWB signal with four sub-bands can be obtained and the transmission capacity can be quadrupled. The feasibility of the proposed scheme is verified by a system demonstration, where the signal modulated by amplitude-phase-shift-key format reaches a high bit rate of 2 Gb/s.

Description: We demonstrated a high-speed mid-infrared frequency modulation spectroscopy scheme based on a distributed-feedback quantum Cascade laser for the application in trace gas sensing by means of all-optical frequency modulation. With this method, the modulation frequency spectrum of a gas sample can be obtained within the mid-infrared pulse duration of $sim 200$ ns. A frequency modulation of the middle infrared lasing optical frequency was achieved in quantum Cascade laser with a modulation frequency of 200 MHz by illuminating with a 1550-nm near-infrared laser. For CO gas, with 2-mW near-infrared illumination, the noise-equivalent sensitivity of the frequency modulation spectroscopy was estimated to be 0.12 ppmv for an absorption length of 6.1 m, indicating an improvement by a factor of 7 compared with Voigt-fitted direct absorption spectroscopy (0.84 ppmv). The frequency modulation signal was found to be proportional to the incident near-infrared power, and therefore, the noise-equivalent sensitivity is expected to be further improved by increasing the near-infrared illumination power. This high-speed frequency modulation spectroscopy based on the distributed-feedback quantum Cascade laser has promising applications in high-speed and high-sensitivity gas sensing.

Description: Second-harmonic generation (SHG) near ultraviolet wavelengths is experimentally demonstrated by coupling femtosecond pump pulses into the normal dispersion region far away from the zero-dispersion wavelength of the fundamental mode in a silica photonic crystal fiber (PCF) fabricated in our laboratory. When the pump pulses with average input power $P_{mathrm {av}}$ of 500 mW and center wavelength $lambda _{mathrm {p}}$ of 820 nm are used, the maximum conversion efficiency $eta _{mathrm {SH}} ^{^{^{}}}$ of the second harmonics centered at 410 nm can be up to $1.6times 10^{-6}$ , corresponding to the output power $P_{mathrm {SH}}$ of 520 nW. By measuring $P_{mathrm {SH}}$ at different PCF lengths and studying the temporal dependence of $P_{mathrm {SH}}$ , it is confirmed that the physical mechanism of SHG is dominated by surface nonlinearity polarization, which is resulted from the local inhomogeneities in the silica core region and at the core-air-silica cladding interface of PCF. Finally, a theoretical model is established to analyze the nonlinear optical process.

Description: The end-to-end (EE) average bit error rate (ABER) performance of a decode-and-forward-based multihop free-space optical communication system is investigated over composite exponentiated Weibull fading channels with nonzero boresight pointing errors considered. In particular, the cumulative distribution function of the aggregated fading channel for non-identically and independently distributed multihop system is derived. Then, the analytical expression of EE ABER with $M$ -ary phase shift keying subcarrier intensity modulation is achieved in terms of Gauss–Laguerre quadrature rule considering aperture averaging effect. The results show that the ABER performance is mainly limited by nonzero boresight pointing errors for larger receiver aperture size. The comparison with Monte Carlo simulation verifies the validity of the proposed ABER model.

Description: We propose and experimentally demonstrate the use of a novel-design long-wavelength VCSEL with high direct-modulation speed to implement 40-Gb/s transmission suitable for optical interconnects and short-reach scenarios. The single-mode operation, together with the use of a common optical bandpass filter, enables a fiber reach in the kilometer range. The VCSEL is characterized and directly modulated at 40 Gb/s with a power consumption of 18.5 mW. Bit-error-rate (BER) performance has been analyzed for a pseudorandom bit sequence (PRBS) of length $2^{7}-1$ as commonly done in most of similar studies, as well as for increasing the PRBS lengths up to $2^{31}-1$ to understand the system limitations when applying more realistic data, so as to induce slower dynamics physical effects such as thermal effects. BER values below common forward error correction threshold are measured without an optical amplification up to 1 km of a single-mode fiber. In particular, error-free operation ( $text {BER}〈10^{-10})$ with 1.6 dB of penalty with respect to the back-to-back case was measured for the PRBS length of $2^{7}-1$ .

Description: We utilize for the first time, to the best of our knowledge, the amplification and de-amplification properties of a fiber-based optical phase-sensitive amplifier (PSA) to implement a flexible, multiple notch filter with optical power extinction ratio given by the internal amplifier gain squared. Our PSA-based filter is completely realized in the optical domain, permitting further downstream optical signal processing, if desired. Direct detection leads to an additional gain-squared enhancement, resulting in electrical filter extinction that is a biquadratic function of the PSA gain. This suggests that the PSAs with even modest optical gains can be used to realize filters with extremely large electrical extinction ratios or notch depths. We characterize filter performance using bit error rates, and demonstrate highly effective isolation of a signal-of-interest from neighboring interferers. Finally, we measure filter extinctions of 20 and 40 dB in the optical and electrical domains, respectively.

Description: A class of inference problems arising in wireless sensor network contexts is addressed, where nodes aim at estimating cooperatively a parameter based on opportunistically gathered measurements. To allow for generality, non-homogeneities are taken into account in the formulation; furthermore, knowledge of the data distribution is not assumed. A novel linear estimation approach is devised based on a hierarchical modeling, able to cope with local conditions (e.g., due to manufacturing differences, diverse classes of nodes, and/or spatial variability) and heterogeneity of the samples (which cannot be controlled in opportunistic inference). The total variance of the hierarchy is derived, then used to optimize the weights of the linear estimator, obtaining in closed-form the minimum-variance solution of the general non-homogeneous inference problem. The proposed approach induces in the optimal weights a scalar parameter as additive term to the local sample sizes; this acts like a Tikhonov regularization on the variance contributions brought by the cooperating nodes, which reflect local conditions and are inversely proportional to the sample size. As a result of this regularization effect, robustness against heavy-tailed variations of the number of measurements available at the different nodes is achieved. A thorough theoretical analysis, corroborated by numerical results, reveals that the proposed distribution-free approach is superior to both linear and weighted least-squares, and can perform closely to the “oracle” minimum-variance estimator.

Description: We deal with the robust design of multiple-input multiple-output (MIMO) waveform covariance matrices that optimize the worst case (over steering mismatches) transmit beampattern assuming either the peak sidelobe level (PSL) or the integrated sidelobe level (ISL) as figure of merit. To this end, we model the uncertainty set associated with each steering vector through two double-sided, potentially non-convex, quadratic constraints. In addition, we force two suitable constraints on the optimization variable. The former accounts for the width of the mainbeam, whereas the latter is either a uniform or a relaxed elemental power requirement allowing to control the amount of transmitted power. We prove that both the mentioned waveform covariance designs lead to non-convex optimization problems which remarkably share some hidden convexity properties. Hence, we devise polynomial time procedures aimed at synthesizing the desired optimal MIMO waveform covariance matrices. Finally, at the analysis stage, we assess the performance of the proposed techniques showing their capability to ensuring improved worst case performance than some counterparts available in open literature.

Description: We consider a single-carrier asynchronous two-way amplify-and-forward relay network, where two single-antenna transceivers exchange information with the help of several single-antenna relay nodes. We assume that the propagation delay of each relaying path, originating from one transceiver, going through a certain relay, and ending at the other transceiver, can be different from those of the other relaying paths. This assumption turns the end-to-end link into a multi-path channel, which produces inter-symbol-interference at the transceivers. In a block transmission/reception scheme, ISI results in inter-block-interference (IBI) between successive transmitted blocks. To combat IBI, cyclic prefix insertion and deletion as well as pre-channel block equalization are used at the two transceivers. Assuming a limited total transmit power budget, we minimize the total mean squared error between the transmitted and received signals at both transceivers by optimally obtaining the transceivers’ transmit powers and the relay beamforming weight vector as well as the pre-channel block equalizers at the transceivers. We prove that this optimization problem leads to a relay selection scheme, where only the relays contributing to one tap of the end-to-end channel impulse response, are turned on and the remaining relays are switched off. We present an efficient method to obtain the optimal values of the design parameters. Our simulation results show that compared to post-channel equalization, the proposed pre-channel equalization technique has the same performance as the post-channel equalization approach does, when the total available power is relatively low compared to the noise power at the transceivers, while offering receiver simplicity.