Abstract
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.
•Information unscented Kalman filter for bearings-only measurements is derived.•The IUKF is distributed by consensus strategy with adaptive weight updating.•For the proposed CoUKF, each sensor platform acts like a fusion center.•Each platform performs very closely to the optimal centralized fusion.•The CoUKF is scalable to large networks and robust against switching topologies.