Continuous discrete minimum error entropy Kalman filter in non-Gaussian noises system

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This paper proposes continuous discrete linear Kalman filtering algorithm based on the minimum error entropy criterion under non-Gaussian noise environments. Traditional Kalman filters struggle in such environments due to their reliance on Gaussian assumptions. Our approach leverages stochastic differential equations to precisely model system dynamics and integrates the minimum error entropy criterion to capture higher-order statistical properties of non-Gaussian noise. Simulations confirm that the proposed algorithm significantly enhances estimation accuracy and robustness compared to conventional methods, demonstrating its effectiveness in handling complex, noisy environments.