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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. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimation fusion in a distributed wireless sensor network (WSN) under the following conditions: (i) sensor noises are contaminated by outliers or gross errors; (ii) process noise and sensor noises are correlated; (iii) cross-correlation among local estimates is unknown. First, to attack the correlation and outliers, a correlated robust Kalman filtering (coR2KF) scheme with weighted matrices on innovation sequences is introduced as local estimator. It is shown that the proposed coR2KF takes both conventional Kalman filter and robust Kalman filter as a special case. Then, a novel version of our internal ellipsoid approximation fusion (IEAF) is used in the fusion center to handle the unknown cross-correlation of local estimates. The explicit solution to both fusion estimate and corresponding covariance is given. Finally, to demonstrate robustness of the proposed coR2KF and the effectiveness of IEAF strategy, a simulation example of tracking a target moving on noisy circular trajectories is included. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]