# 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.

## Key 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.