Distributed adaptive cubature information filtering for bounded noise system in wireless sensor networks

Abstract This article is concerned with the nonlinear state estimation for the multisenor networked system with uncertain bounded noise. Traditional distributed methods only pay attention to the information fusion of state estimations, but neglect the fusion of noise statistics. The difference of noise statistics among sensor nodes usually affects the precision of state estimation in wireless sensor networks, especially for the distributed state estimation. In this article, in order to improve the accuracy of noise statistic estimations, a distributed noise statistic estimator is derived based on covariance intersection criterion and modified Sage–Husa maximum posterior. Then, distributed adaptive cubature information filtering (DACIF) is founded based on weighted average consensus to obtain accurate state estimation. Two‐step information fusion, including the information fusion of state estimations and noise statistics, is derived to enhance the precision of state estimations. Meanwhile, a novel weighted rule is devised based on the state and measurement innovation vectors to improve the accuracy of distribution information fusion. Next, the estimation errors of DACIF are proved to be bounded in mean square. Simulations and semisimulation experiments are conducted to verify the effectiveness of the proposed algorithm.