Adaptive Consensus-Based Distributed Target Tracking in Sensor Networks

Abstract

This paper is concerned with the distributed Kalman state estimation with an adaptive consensus factor for a discrete-time target linear system over a sensor network. Both optimal filter gain and average disagreement of the estimates are considered in the filter design. In order to estimate the state of the target more accurately, an optimal Kalman gain is obtained by minimizing the mean-squared estimation error. The considered disagreement is employed to adjust the optimal gain as well as to acquire a better filtering performance. An illustrative example has been presented to prove the correctness of the conclusion and show the tracking performance of the filters.