ALBERT

Beschreibung: Underlay cognitive radios (UCRs) allow a secondary user to enter a primary user's spectrum through intelligent utilization of multiuser channel quality information (CQI) and sharing of codebook. The aim of this work is to study two-user Gaussian UCR systems by assuming the full or partial knowledge of multiuser CQI. Key contribution of this work is motivated by the fact that the full knowledge of multiuser CQI is not always available. We first establish a location-aided UCR model where the secondary user is assumed to have partial CQI about the secondary-transmitter to primary-receiver link as well as full CQI about the other links. Then, new UCR approaches are proposed and carefully analyzed in terms of the secondary user's achievable rate, denoted by C2, the capacity penalty to primary user, denoted by ΔC1, and capacity outage probability. Numerical examples are provided to visually compare the performance of UCRs with full knowledge of multiuser CQI and the proposed approaches with partial knowledge of multiuser CQI.

Beschreibung: A novel ranging technique based on received signal strength (RSS) and suitable to indoor scenarios is illustrated. In the proposed technique, multiple power measurements, associated with the signals radiated by a cluster of nodes surrounding a given target, are jointly processed to improve the quality of RSS-based estimation of the distance between the target and an anchor. Specific algorithms for the generation of a cluster and for the acquisition of power measurements are described. Simulation results show that, when used in indoor positioning systems, the proposed ranging technique is substantially more accurate than noncooperative strategies. In addition, it allows to concentrate significant processing tasks in a limited number of fixed anchors, so reducing maintenance costs and making it possible to adopt cheap and simple portable wireless nodes.

Beschreibung: It is a valid way to analyze the space object real-time movement by using distributed satellite earth observation system, which can provide the stereographic image through the collaboration of the satellites. That relative position and pose estimation is one of the key technologies for distributed micro/nanosatellite earth observation system (DMSEOS). In this paper, on the basis of the attitude dynamics of spacecrafts and the theory of machine vision, an autonomous positioning and orientating algorithm for distributed micro/nanosatellites based on dual quaternion and EKF (extended Kalman filtering) is proposed. Firstly, how to represent a line transform unit using dual quaternion is introduced. Then, the feature line point of the line transform unit is defined. And then, on the basis of the attitude dynamics of spacecrafts and the theory of EKF, we build the state and observation equations. Finally, the simulations show that this algorithm is an accurate valid method in positioning and orientating of distributed micro/nanosatellite earth observation system.

Beschreibung: Multipath remains a dominant source of ranging errors in Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS) or the future European satellite navigation system Galileo. Multipath is generally considered undesirable in the context of GNSS, since the reception of multipath can make significant distortion to the shape of the correlation function used for time delay estimation. However, some wireless communications techniques exploit multipath in order to provide signal diversity though in GNSS, the major challenge is to effectively mitigate the multipath, since we are interested only in the satellite-receiver transit time offset of the Line-Of-Sight (LOS) signal for the receiver's position estimate. Therefore, the multipath problem has been approached from several directions in order to mitigate the impact of multipath on navigation receivers, including the development of novel signal processing techniques. In this paper, we propose a maximum likelihood-based technique, namely, the Reduced Search Space Maximum Likelihood (RSSML) delay estimator, which is capable of mitigating the multipath effects reasonably well at the expense of increased complexity. The proposed RSSML attempts to compensate the multipath error contribution by performing a nonlinear curve fit on the input correlation function, which finds a perfect match from a set of ideal reference correlation functions with certain amplitude(s), phase(s), and delay(s) of the multipath signal. It also incorporates a threshold-based peak detection method, which eventually reduces the code-delay search space significantly. However, the downfall of RSSML is the memory requirement which it uses to store the reference correlation functions. The multipath performance of other delay-tracking methods previously studied for Binary Phase Shift Keying-(BPSK-) and Sine Binary Offset Carrier- (SinBOC-) modulated signals is also analyzed in closed loop model with the new Composite BOC (CBOC) modulation chosen for Galileo E1 signal. The simulation results show that the RSSML achieves the best multipath mitigation performance in a uniformly distributed two-to-four paths Rayleigh fading channel model for all three modulated signals.

Beschreibung: Indoor handset localization in an urban apartment setting is studied using GSM trace mobile measurements. Nearest-neighbor, Support Vector Machine, Multilayer Perceptron, and Gaussian Process classifiers are compared. The linear Support Vector Machine provides mean room classification accuracy of almost 98% when all GSM carriers are used. To our knowledge, ours is the first study to use fingerprints containing all GSM carriers, as well as the first to suggest that GSM can be useful for localization of very high performance.

Beschreibung: The Global Positioning System (GPS) has become one of the state-of-the-art location systems that offers reliable mobile terminal (MT) location estimates. However, there exist situations where GPS is not available, for example, when the MT is used indoors or when the MT is located close to high buildings. In these scenarios, a promising approach is to combine the GPS-measured values with measured values from the Global System for Mobile Communication (GSM), which is known as hybrid localization method. In this paper, three nonlinear filters, namely, an extended Kalman filter, a Rao-Blackwellized unscented Kalman filter, and a modified version of the recently proposed cubature Kalman filter, are proposed that combine pseudoranges from GPS with timing advance and received signal strengths from GSM. The three filters are compared with each other in terms of performance and computational complexity. Posterior Cramér-Rao lower bounds are evaluated in order to assess the theoretical performance. Furthermore, it is investigated how additional GPS reference time information available from GSM influences the performance of the hybrid localization method. Simulation and experimental results show that the proposed hybrid method outperforms the GSM method.

Beschreibung: The term inertial navigation is often automatically associated with the term initial value problem. However, there are many applications where it is possible to end up with a boundary value problem (BVP) as well. We show that in case of a BVP, the finite element method that incorporates boundary conditions can be efficiently used to compute position and velocity estimates not prone to accumulation of errors. For further accuracy enhancements, a method of combining inertial measurements with additional constraints is proposed. This way, we can model sensor errors, known to limit the accuracy of the system. The capabilities of the proposed methods are demonstrated with real-life examples.

Beschreibung: Present land vehicle navigation relies mostly on the Global Positioning System (GPS) that may be interrupted or deteriorated in urban areas. In order to obtain continuous positioning services in all environments, GPS can be integrated with inertial sensors and vehicle odometer using Kalman filtering (KF). For car navigation, low-cost positioning solutions based on MEMS-based inertial sensors are utilized. To further reduce the cost, a reduced inertial sensor system (RISS) consisting of only one gyroscope and speed measurement (obtained from the car odometer) is integrated with GPS. The MEMS-based gyroscope measurement deteriorates over time due to different errors like the bias drift. These errors may lead to large azimuth errors and mitigating the azimuth errors requires robust modeling of both linear and nonlinear effects. Therefore, this paper presents a solution based on Parallel Cascade Identification (PCI) module that models the azimuth errors and is augmented to KF. The proposed augmented KF-PCI method can handle both linear and nonlinear system errors as the linear parts of the errors are modeled inside the KF and the nonlinear and residual parts of the azimuth errors are modeled by PCI. The performance of this method is examined using road test experiments in a land vehicle.

Beschreibung: As part of the GPS modernization, the GPS L1 C/A signal will be augmented by the L1C signal. With this improvement, for the first time, several signals from the same constellation will be available at the same frequency. In this paper, an acquisition method is implemented to combine the GPS L1 C/A and L1C signals before correlation. The combined acquisition succeeds to acquire the signal at low C/N0, whereas the acquisition of the L1C data channel alone fails. Concerning the tracking, a method to combine the GPS L1 C/A and L1C signals before the discriminator is developed. This method shows better performance than the traditional tracking using only one signal. Finally, a Kalman filter to combine the signals in the tracking is developed. It shows better performance than the traditional tracking in all conditions. Since the L1C signal will not be broadcast before 2013, these methods are tested using a software signal simulator.