An Improved Cubature Information Filtering for Target Tracking in Distributed Wireless Sensor Networks with Measurement Outliers

Zhang, Jiahao; Gao, Shesheng; Gao, Bingbing; Wei, Wenhui; Yang, Jiahui

doi:10.1109/ccdc.2019.8832567

Cited by 3 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, 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. Nevertheless, the DCIF cannot deal with state estimation under extremely large measurement outliers, and estimation errors of the distributed CIF are not proved to be bounded in mean square.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Gao

et al. 2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

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. INDEX TERMS Robust cubature information filtering, maximum correntropy criterion, Non-Gaussian measurement noise, distributed state estimation, weighted average consensus.

show abstract

Section: Introductionmentioning

confidence: 99%