Distributed Kalman Consensus Filter for Estimation With Moving Targets

Consensus-based distributed Kalman filters for estimation with targets have attracted considerable attention. Most of the existing Kalman filters use the average consensus approach, which tends to have a low convergence speed. They also rarely consider the impacts of limited sensing range and target mobility on the information flow topology. In this article, we address these issues by designing a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation in continuous time. A new moving target information-flow topology for the measurement of targets is developed based on the sensors' sensing ranges, targets' random mobility, and local information-weighted neighbors. Novel necessary and sufficient conditions about the convergence of the proposed DKCF are developed. Under these conditions, the estimates of all sensors converge to the consensus values. Simulation and comparative studies show the effectiveness and the superiority of this new DKCF.

Automated summary

In this article, we propose a novel distributed Kalman consensus filter (DKCF) with an information-weighted consensus structure for random mobile target estimation. We address the issues of low convergence speed and limited sensing range and target mobility in existing Kalman filters. Our simulations and comparative studies demonstrate the effectiveness and superiority of the proposed DKCF.