Distributed Maximum Correntropy Kalman Filtering for Stochastic Discrete Time system: A Encoding-Decoding-Based Approach

doi:10.1016/j.jfranklin.2026.108498

Distributed Maximum Correntropy Kalman Filtering for Stochastic Discrete Time system: A Encoding-Decoding-Based Approach

2026-02-10

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DOI：10.1016/j.jfranklin.2026.108498

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In this article, the encoding-decoding-based distributed maximum correntropy Kalman filtering is investigated for discrete-time stochastic systems subject to non-Gaussian noise. In order to improve the communication reliability, an encoding-decoding mechanism is applied to transmit the data under non-Gaussian environments. The distributed maximum correntropy Kalman filter is designed based on the measurements after the encoding-decoding process, such that both the filter gains and the upper bound of the filtering error covariance are recursively presented through using a fixed-point iterative update rule. Finally, a numerical simulation is given to verify the validity of the developed distributed maximum correntropy Kalman filtering strategy.

Keywords:

Distributed Kalman Filtering

Maximum Correntropy

Encoding-Decoding Mechanism

Non-Gaussian Noise

Stochastic Systems

Distributed Maximum Correntropy Kalman Filtering for Stochastic Discrete Time system: A Encoding-Decoding-Based Approach

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