Abstract
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.
Highlights
With the development of communication, cloud computing and embedded technology, wireless sensor technology has been getting increased attention in recent years [1], and wireless sensor networks (WSN) is gradually applied to navigation, environment monitoring, and target tracking etc
maximum correntropy criterion (MCC)-UKF and MCC based cubature Kalman filtering (MCC-CKF) are designed for state estimation on a single sensor node, but distributed cubature information filtering (DCIF), DRCIF1 and distributed RCIF (DRCIF) are derived for state evaluation in distribution WSN
In the local sensor node, MCC based robust CIF (RCIF) is derived to deal with measurement outliers
Summary
With the development of communication, cloud computing and embedded technology, wireless sensor technology has been getting increased attention in recent years [1], and WSN is gradually applied to navigation, environment monitoring, and target tracking etc. The designed MCC based nonlinear methods is only suitable for state estimation in a single sensor node, but not applicable for state evaluation in distributed WSN. A distributed cubature information filtering based on MCC is designed in [26] to cope with measurement outliers in WSN. We present a novel method for state estimation under measurement outliers or impulse noise in WSN. WAC based DRCIF is derived for distributed information fusion in WSN to enhance the accuracy of state estimation and the stability of WSN. The main contributions of this paper are given as follows: 1) A novel distributed robust filtering is derived for nonlinear systems to cope with measurement outliers in WSN, extremely large outliers.
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