Consensus-based cubature information filtering for sensor networks with incomplete measurements

Abstract

Consensus-based distributed filtering is an effective technique for state estimation or data fusion over sensor networks. However, nonlinearity of systems as well as network-induced incomplete information problems are the main obstacles on the way. In this paper, we deal with the distributed filtering of nonlinear systems over sensor networks with incomplete measurements. A perception of credibility evaluation on nodes’ estimations is presented, followed by a novel credibility weight design method. The weights are redistributed by nodes’ estimations in a normal fashion at each time step. The convergence of data fusion with the weights is proved. Further the credibility weight method is integrated into the general consensus-based cubature information filtering algorithm to handle various incomplete measurement problems. Numerical experiments, in the case of unknown noise statistics, measurement interference and measurement missing, demonstrate the effectiveness and robustness of the proposed algorithm.