Consensus Filtering Algorithm with Incomplete Measurements

Abstract

In order to improve the estimate performance of distributed sensor networks, we introduce an improved consensus-based distributed filtering algorithm. Firstly, sensors obtain the local estimates using their own measurements. Then through utilizing the data of neighbor nodes to update the local estimates, estimates in the networks can reach dynamic average consensus. Based on studying the value of consensus step size, we give a sufficient condition for the convergence of the algorithm and discusses the influences of the consensus step size and the detection probability on the accuracy and consensus of estimation. The numerical simulation demonstrates that the algorithm proposed in this paper can improve the accuracy and consensus of estimation, and it is more robust with incomplete measurements.