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

Area of Science:

• Aerospace Engineering
• Radar Systems
• Signal Processing

Background:

• Hypersonic glide targets (HGTs) present significant radar tracking challenges due to complex maneuvers and non-Gaussian measurement noise.
• Existing single-model and multiple-model tracking methods struggle with maneuver mode capture, model set completeness, computational efficiency, and non-Gaussian noise suppression.

Purpose of the Study:

• To develop an advanced multiple-model tracking method for hypersonic glide targets (HGTs).
• To address limitations in existing methods concerning maneuver complexity, noise characteristics, and computational efficiency.

Main Methods:

• A Hierarchical Adaptive Moment Matching (HAMM) multiple-model method is proposed.
• A comprehensive maneuver mode model set is constructed.
• A hierarchical framework dynamically adapts a coarse model set using posterior probability evolution and Rényi divergence.
• A fine model set is generated via moment matching.
• A minimum error entropy cubature Kalman filter (MEECKF) is introduced to handle non-Gaussian noise.

Main Results:

• The proposed HAMM method demonstrates improved positioning accuracy for HGTs.
• The method exhibits faster convergence compared to conventional approaches.
• The MEECKF effectively suppresses non-Gaussian measurement noise, enhancing tracking stability.

Conclusions:

• The HAMM multiple-model method offers a robust solution for radar tracking of HGTs.
• The integration of hierarchical model adaptation and non-Gaussian noise suppression provides superior performance.
• The proposed approach advances the state-of-the-art in hypersonic target tracking.