Unscented Kalman Filter With Enhanced Mixture Minimum Error Entropy for Robust Ship Integrated Electric Propulsion System State Estimation

In view of abnormal situations, such as multimodal noise, bad measurement data, and sudden load changes in a ship integrated electric propulsion system, this article proposes an unscented Kalman filter with enhanced mixture minimum error entropy (EnMMEE-UKF) to estimate the dynamic state of a ship integrated electric propulsion system. The new algorithm mixture includes two Gaussian functions, defines them as kernel functions, and uses quantization operators to lighten the weight of the mixture minimum error entropy (MMEE). This not only reduces the calculation burden of the MMEE but also effectively improves the system’s ability to handle strongly nonlinear models. In addition, in the lightweight MMEE (LMMEE)-unscented Kalman filter (UKF) algorithm, a fading factor is introduced, based on the strong tracking filter (STF) theory, to modify the error covariance matrix. The enhanced MMEE replaces the mean square error loss of the traditional UKF algorithm. This enhances the robustness of the algorithm estimation, in which a statistical linearization method is adopted to merge the state and measurement errors into the cost function of the enhanced MMEE. Finally, a fixed-point iteration method is applied to recursively update the posterior estimate and covariance matrix. To verify the effectiveness of the proposed method, numerical simulation experiments of various scenarios are conducted on the simulation system and the prototype system. The results demonstrate that the proposed algorithm not only has high estimation accuracy under various abnormal conditions but also effectively reduces the computational complexity of the algorithm, making its application in engineering possible.