Distributed maximum correntropy Kalman filter with state equality constraints in a sensor network with packet drops

This study presents the design of a distributed Kalman filter with excellent performance in a sensor network with packet drops and non-Gaussian noise. The proposed filter incorporates the maximum correntropy criterion and state equality constraint information. The maximum correntropy criterion is employed to handle non -Gaussian noise by utilizing higher-order statistics. To achieve superior estimation performance, the equality constrained filter is obtained by incorporating state equality constraint information. Furthermore, to enhance the consistency in the estimation of each node, the equality constrained distributed maximum correntropy Kalman filter is established through the covariance interaction algorithm. Finally, the effectiveness of the proposed filter is verified by simulation results.

Automated summary

This study presents the design of a distributed Kalman filter that performs well in a sensor network with packet drops and non-Gaussian noise. The proposed filter combines the maximum correntropy criterion and state equality constraint information. It handles non-Gaussian noise using higher-order statistics and achieves superior estimation performance by incorporating state equality constraint information. The effectiveness of the filter is verified through simulation results.