Adaptive Gate Based on Maneuvering Probability for Space Target Tracking

This is the authors' abstract. We don't add key points for this paper.

This paper introduces an adaptive gate scaling method to address the complex challenge of tracking an increasing number of maneuvering space targets in cluttered environments with low data rates. The method involves two key modules: the Maneuver Detection (MD) module, which employs an improved logarithmic maneuvering probability algorithm, and the Total Gate Amplification Factor (TGAF) module, utilizing a sigmoid function to dynamically adjust gate size based on maneuvering probabilities. To ensure gate stability, we utilize the standard deviation of measurement-prediction differences during non-maneuvering periods. Simulations demonstrate our method's effectiveness in scaling the gate adaptively and minimizing clutter, thus enhancing correlation accuracy and filtering performance.