Distributed Robust Cubature Information Filtering for Measurement Outliers in Wireless Sensor Networks

In wireless sensor networks (WSN), measurements are always corrupted by outliers or impulsive noise. Cubature information filtering (CIF) is founded based on minimum mean square error (MMSE) criterion, which is not applicable to non-Gaussian noise. Hence, a novel robust CIF (RCIF) is derived based on maximum correntropy criterion (MCC) to enhance the robustness of state estimation in the local node. For the information fusion, weighted average consensus (WAC) based distributed RCIF (DRCIF) is founded to improve the stability of sensor networks and the accuracy of state estimation. The estimation error of DRCIF is proved to be bounded in mean square. Numerical simulations are provided to evaluate the effectiveness of proposed algorithms.