The Labeled Square Root Cubature Information GM-PHD Approach for Multi Extended Targets Tracking.

Area of Science:

• Radar signal processing
• Target tracking algorithms
• Data association techniques

Background:

• Conventional point target tracking fails with high-resolution radar observations of extended targets.
• Existing Extended Target Gaussian Mixture Probability Hypothesis Density (ET-GM-PHD) methods have limitations with nonlinear target motion and state-trajectory association.

Purpose of the Study:

• To develop an improved ET-GM-PHD approach for tracking extended targets with nonlinear dynamics.
• To address the challenge of managing multiple extended targets and their trajectories in radar systems.

Main Methods:

• Utilized the square root cubature information filter (SRCIF) for predicting and updating Gaussian Mixture (GM) components within the ET-GM-PHD framework.
• Implemented a candidate observation extraction method to reduce computational cost during GM component updating.
• Introduced a label-based trajectory construction method to associate estimated states with specific target trajectories.

Main Results:

• The proposed labeled ET-GM-PHD approach effectively tracks extended targets exhibiting nonlinear motion.
• The method successfully obtains simultaneous state and trajectory estimations for multiple extended targets.
• Simulation results validated the effectiveness and improved performance of the developed approach.

Conclusions:

• The labeled ET-GM-PHD approach based on SRCIF offers a robust solution for extended target tracking in scenarios with nonlinear dynamics.
• The label-based trajectory management significantly enhances the ability to handle multiple extended targets.
• This work advances radar tracking capabilities for complex target scenarios.