A novel fusion entropy Kalman filter under parallel IMM framework for intermittent observation systems

Abstract

This paper proposes a novel fusion entropy Kalman filter with intermittent observations under the parallel interacting multiple model framework (PIMM-FEIOKF), designed to enhance state estimation in complex scenarios involving intermittent observations, target maneuvers, and non-Gaussian noise. Specifically, the PIMM-FEIOKF employs a fusion entropy method to integrate two interacting multiple model filters with intermittent observations: the maximum correntropy Kalman filter (IMM-MCIOKF) and the minimum error entropy Kalman filter (IMM-MEEIOKF). Both filters rely on the same connectivity matrix that guarantees the conditions for Cholesky decomposition, ensuring the smooth execution of state estimation updates. The PIMM-FEIOKF algorithm runs the two filters in parallel and dynamically selects model probabilities through a transfer probability correction function. This approach achieves a balance between the computational efficiency of IMM-MCIOKF and the high precision of IMM-MEEIOKF. Furthermore, it leverages both current and past model information to improve estimation performance. Simulation results demonstrate that the proposed PIMM-FEIOKF enhances position and velocity accuracy by 12.2% and 7.4%, respectively, compared to the advanced IMM-MEEIOKF. These findings underscore the robustness and efficiency of PIMM-FEIOKF in addressing challenging scenarios, showcasing its superiority over traditional methods.