Maximum Correntropy Criterion-Based Robust Fuzzy Adaptive Unscented Kalman Filter for Target Tracking With Model Mismatches

This paper investigates the robust state estimation problem for non-cooperative target tracking to overcome the limitations of conventional tracking algorithms under model uncertainty and noise uncertainty. Although some existing algorithms employ the maximum correntropy criterion (MCC) to handle non-Gaussian noise, most of them generally assume precise state-space models and neglect the effect of uncertain noise parameters as well. To deal with this limitation, a robust fuzzy adaptive MCC-based unscented Kalman filter (UKF) algorithm is proposed, which enhances robustness through the simultaneous state and unknown input estimation (SSUIE), and adjusts the noise covariance via a fuzzy inference system (FIS) adaptively. First, an unknown input (UI) estimator is integrated into the MCC-based UKF to tackle the challenge of model uncertainty, thereby eliminating the reliance on a precise state-space model. Then, an FIS is employed to adjust noise covariance, which explicitly accounts for uncertain noise statistics, improving adaptability in challenging conditions. Notably, the FIS-based adjustment is a more efficient and flexible method compared to the interacting multiple model (IMM) and variational Bayesian (VB) methods, as it avoids the need for precise prior knowledge and high computational complexity. Finally, the simulation results demonstrate that our proposed algorithm can enable effective tracking of non-cooperative targets, even during orbital maneuvers and under uncertain noise conditions. This work provides an adaptive solution for enhancing estimation robustness against uncertain target behaviors and dynamic environments, with potential applications in autonomous systems and security defense systems.