Adaptive Consensus-Based Distributed Target Tracking in Sensor Networks With Limited Sensing Range

In this brief, consensus-based algorithms for distributed target tracking are discussed, and a new algorithm with decentralized adaptation is proposed for sensor networks with limited sensing range. The given comparative analysis shows that tuning of the gains in the consensus scheme is of crucial importance for getting simple, yet efficient tracking algorithms, requiring the exchange of only local state estimates between the nodes. The proposed algorithm, which gives higher importance to the nodes currently receiving measurements, is based on the exchange of additional binary information and represents a robust and efficient tool for practice. Stability of the algorithm is theoretically discussed on a given scenario. Selected examples illustrate its performance in terms of the estimation error and the disagreement between the nodes.