Fuzzy Inference System-Enhanced Adaptive Sliding Innovation Filter for Non-Cooperative Target Tracking

Abstract

This letter proposes a novel adaptive sliding innovation filter (SIF) enhanced by a fuzzy inferencesystem (FIS), which aims to improve estimation robustness for non-cooperative target tracking. The main contributions include: first, an FIS-enhanced adaptive adjustment scheme for the sliding boundary layer (SBL) is proposed, which improves the tracking performance in dynamic and uncertain environments; second, the SBL width is designed as a vector, which better adapts to measurements with different characteristics and magnitudes; third, an innovation-related indicator is designed as the input of the FIS, which is capable of detecting faults without distributional assumptions, thereby allowing the proposed algorithm to handle system uncertainties effectively. Through the adaptive parameter adjustment of the proposed algorithm, the tracking performance is improved under uncertain conditions, such as maneuver-induced model mismatches and noise uncertainties. An experiment on non-cooperative orbital target tracking is provided to validate the theoretical advancements, demonstrating the proposed filter’s superior robustness and convergence speed compared to both conventional SIF and unscented Kalman filter (UKF) algorithms.