ALBERT

Description: Indoor wireless localization using Bluetooth Low Energy (BLE) beacons has attracted considerable attention after the release of the BLE protocol. In this paper, we propose an algorithm that uses the combination of channel-separate polynomial regression model (PRM), channel-separate fingerprinting (FP), outlier detection and extended Kalman filtering (EKF) for smartphone-based indoor localization with BLE beacons. The proposed algorithm uses FP and PRM to estimate the target’s location and the distances between the target and BLE beacons respectively. We compare the performance of distance estimation that uses separate PRM for three advertisement channels (i.e., the separate strategy) with that use an aggregate PRM generated through the combination of information from all channels (i.e., the aggregate strategy). The performance of FP-based location estimation results of the separate strategy and the aggregate strategy are also compared. It was found that the separate strategy can provide higher accuracy; thus, it is preferred to adopt PRM and FP for each BLE advertisement channel separately. Furthermore, to enhance the robustness of the algorithm, a two-level outlier detection mechanism is designed. Distance and location estimates obtained from PRM and FP are passed to the first outlier detection to generate improved distance estimates for the EKF. After the EKF process, the second outlier detection algorithm based on statistical testing is further performed to remove the outliers. The proposed algorithm was evaluated by various field experiments. Results show that the proposed algorithm achieved the accuracy of 〈2.56 m at 90% of the time with dense deployment of BLE beacons (1 beacon per 9 m), which performs 35.82% better than 〈3.99 m from the Propagation Model (PM) + EKF algorithm and 15.77% more accurate than 〈3.04 m from the FP + EKF algorithm. With sparse deployment (1 beacon per 18 m), the proposed algorithm achieves the accuracies of 〈3.88 m at 90% of the time, which performs 49.58% more accurate than 〈8.00 m from the PM + EKF algorithm and 21.41% better than 〈4.94 m from the FP + EKF algorithm. Therefore, the proposed algorithm is especially useful to improve the localization accuracy in environments with sparse beacon deployment.

Description: Remote Sensing, Vol. 9, Pages 971: Examining the Influence of Crop Residue Burning on Local PM2.5 Concentrations in Heilongjiang Province Using Ground Observation and Remote Sensing Data Remote Sensing doi: 10.3390/rs9100971 Authors: Ziyue Chen Danlu Chen Yan Zhuang Jun Cai Na Zhao Bin He Bingbo Gao Bing Xu Although a many studies concerning crop residue burning have been conducted, the influence of crop residue burning on local PM2.5 concentrations remains unclear. The number of crop residue burning spots was the highest in Heilongjiang province and we extracted crop residue burning spots for this region using MOD14A1 (Thermal Anomalies & Fire Daily L3 Global 1 km) data and national land cover data. By analyzing the temporal variation of crop residue burning and PM2.5 concentrations in Heilongjiang province, we found that the total number of crop residue burning spots was not correlated with the variations of PM2.5 concentrations at a provincial (regional) scale. However, crop residue burning exerted notable influence on the variations of PM2.5 concentrations at a local scale. We experimented with a set of buffer zone radiuses to examine the influencing area of crop residue burning. The results suggest that the valid influencing area of crop residue burning was between 50 and 80 km. The mean PM2.5 concentration measured at stations close to crop residue burning spots was more than 60 μg/m3 higher than that measured at stations not close to crop residue burning spots. However, no consistent, significant correlation existed between the existence of crop residue burning spots and local PM2.5 concentrations, indicating that local PM2.5 concentrations were influenced by a diversity of factors and not solely controlled by crop residue burning. This research also provides suggestions for better understanding the role of crop residue burning in local and regional air pollution.

Description: Sensors, Vol. 18, Pages 1482: A Double Dwell High Sensitivity GPS Acquisition Scheme Using Binarized Convolution Neural Network Sensors doi: 10.3390/s18051482 Authors: Zhen Wang Yuan Zhuang Jun Yang Hengfeng Zhang Wei Dong Min Wang Luchi Hua Bo Liu Longxing Shi Conventional GPS acquisition methods, such as Max selection and threshold crossing (MAX/TC), estimate GPS code/Doppler by its correlation peak. Different from MAX/TC, a multi-layer binarized convolution neural network (BCNN) is proposed to recognize the GPS acquisition correlation envelope in this article. The proposed method is a double dwell acquisition in which a short integration is adopted in the first dwell and a long integration is applied in the second one. To reduce the search space for parameters, BCNN detects the possible envelope which contains the auto-correlation peak in the first dwell to compress the initial search space to 1/1023. Although there is a long integration in the second dwell, the acquisition computation overhead is still low due to the compressed search space. Comprehensively, the total computation overhead of the proposed method is only 1/5 of conventional ones. Experiments show that the proposed double dwell/correlation envelope identification (DD/CEI) neural network achieves 2 dB improvement when compared with the MAX/TC under the same specification.