Distributed Correntropy Kalman Filtering for Robust State Estimation


 Distributed Correntropy Kalman Filtering for Robust State Estimation


This approach enhances the classical Kalman filter using distributed correntropy theory to improve robustness against non-Gaussian noise and outliers. By integrating kernel-based similarity measures with decentralized estimation algorithms, the method enables accurate state prediction across sensor networks.

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