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Wen Q.,Xiangtan University | Zhou Y.,Xiangtan University | Zhou Y.,Key Laboratory of Intelligent Computing and Information Processing of MOE | Hu L.,Xiangtan University | And 2 more authors.
Proceedings of 2015 International Conference on Estimation, Detection and Information Fusion, ICEDIF 2015 | Year: 2015

Target tracking is one of the most important applications for wireless sensor networks (WSNs). It is usually assumed that the knowledge of the sensor nodes' position is known precisely. However, practically nodes are randomly deployed without prior knowledge about their own positions. In this situation, simultaneous localization and tracking (SLAT) is necessary and is receiving more and more research interest during the last few years. In this paper, several popular and practical filtering techniques are reviewed and compared for the problem of SLAT, including extended Kalman filtering (EKF), unscented Kalman filtering (UKF), and interactive multiple model (IMM). Simulation examples are included to demonstrate the superiority and shortcoming of each method. Results show that compared with other methods, IMM based on UKFs has better accuracy in both localization and tracking, as well as higher robustness. © 2015 IEEE. Source


Zhou Y.,Xiangtan University | Zhou Y.,Key Laboratory of Intelligent Computing and Information Processing of MOE | Wang D.,Xiangtan University | Lan Y.,Xiangtan University | Wen Q.,Xiangtan University
International Journal of Distributed Sensor Networks | Year: 2014

The problem of consensus-based distributed tracking in wireless sensor networks (WSNs) with switching network topologies and outlier-corrupted sensor observations is considered. First, to attack the outlier-corrupted measurements, a robust Kalman filtering (RKF) scheme with weighted matrices on innovation sequences is introduced. The proposed RKF possesses high robustness against outliers while having similar computational burden as traditional Kalman filter. Then, each node estimates the network-wide agreement on target state using only communications between one-hop neighbors. In order to improve the convergent speed of the consensus filter in case of switching topologies, an adaptive weight update strategy is proposed. Note that the proposed algorithm relaxes the requirement of Gaussian noise statistics in contrast to the decentralized/distributed Kalman filters. Besides, unlike the existing consensus-based filters, we do not need to perform consensus filtering on the covariance matrices, which will reduce the computational and communicational burden abundantly. Finally, simulation examples are included to demonstrate the robustness of the proposed RKF and effectiveness of adaptive consensus approach. © 2014 Yan Zhou et al. Source


Zhou Y.,Xiangtan University | Zhou Y.,Key Laboratory of Intelligent Computing and Information Processing of MOE | Wang D.,Xiangtan University | Wang D.,Key Laboratory of Intelligent Computing and Information Processing of MOE | Li J.,Shanghai JiaoTong University
Signal Processing | Year: 2014

Target tracking in bearings-only sensor networks (BOSNs) has obtained distinct interest in the last decade. In this situation, the scalability of the tracking algorithm and robustness against network topologies due to moving platform or node/communication fault are two important issues. This motivates the present work on distributed bearings-only tracking in switching BOSNs adopting consensus-based unscented Kalman filters (CoUKFs). First, information unscented Kalman filters (IUKFs) for bearings-only measurements are derived by statistical linearization approach. Then the IUKF is distributed by computing the average consensus on information contribution with only message exchange between one-hop neighbors. To accelerate the convergence in switching networks, adaptive updating of the weights in terms of gradient is proposed for the consensus strategy. Finally, an example of tracking by a network of mixed static and moving bearings-only sensors with switching topologies is given to demonstrate the effectiveness of the proposed method. © 2014 Elsevier B.V. Source

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