Distributed consensus strong tracking filter for wireless sensor networks with model mismatches

A distributed consensus strong tracking filter is developed and investigated for the target tracking problems with model mismatches in wireless sensor networks. This novel approach is based on basic strong tracking filter which is one of the most efficient and robust state estimation algorithms for model mismatches. However, strong tracking filter encounters two fundamental problems in wireless sensor networks: communication congestion and scalability. This work is to apply a distributed way of strong tracking filter using the consensus filter to adjust the time-variant fading factor in a distributed manner, which makes the residual error sequences of all sensors keep orthogonality with the state estimation errors. Theoretical analysis shows that the calculation flow diagram of distributed consensus strong tracking filter is as complex as that of distributed Kalman filtering. Although the message of distributed consensus strong tracking filter is approximately twice the size of the message of distributed Kalman filtering, distributed consensus strong tracking filter has better accuracy in target tracking with model mismatches. Finally, simulation results are provided to show that the state estimation of distributed consensus strong tracking filter has better accuracy and robustness against target mutation than the traditional distributed Kalman filtering when the tracker is described by current statistic model.