A Hierarchical Adaptive Moment Matching Multiple Model Tracking Method for Hypersonic Glide Target Under Measurement Uncertainty

TL;DR

This paper introduces a new tracking method for hypersonic glide targets that improves accuracy and efficiency under uncertain measurements.

Contribution

The novel Hierarchical Adaptive Moment Matching (HAMM) method dynamically adapts model sets and suppresses non-Gaussian noise for better tracking.

Findings

The proposed HAMM method improves positioning accuracy for hypersonic glide targets.

The MEECKF filter effectively suppresses non-Gaussian measurement noise.

Monte Carlo simulations show faster convergence and better performance compared to existing methods.

Abstract

Hypersonic glide targets (HGTs) pose significant challenges for radar tracking due to complex maneuver strategies and time-varying statistics of measurement noise. Conventional single-model tracking methods are generally insufficient to fully capture maneuver modes, while existing multiple-model methods face trade-offs between model set completeness and computational efficiency. In addition, existing tracking methods struggle to cope with the non-Gaussian noise during hypersonic flight. To overcome these limitations, a Hierarchical Adaptive Moment Matching (HAMM) multiple-model method is proposed in this paper. Firstly, a comprehensive model set is constructed to cover characteristic maneuver modes. Subsequently, a hierarchical multiple-model framework is developed where: (1) a coarse model set is dynamically adapted by multi-frame posterior probability evolution and Rényi divergence criteria; (2) a fine model set is generated based on the moment matching method. Furthermore, the minimum error entropy cubature Kalman filter (MEECKF) is proposed to suppress the non-Gaussian measurement noise with high stability. Monte Carlo simulations demonstrate that the proposed method achieves improved positioning accuracy and faster convergence.

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