A Fast Time-Adaptive Data Association Method for Multi-Target Tracking with Discontinuous Sparse LEO Satellite...

Area of Science:

• Remote Sensing
• Data Association
• Maritime Surveillance

Background:

• Low-Earth-orbit (LEO) satellite constellations face challenges in surface maritime target detection due to discontinuous observations, non-uniform intervals, and clutter.
• Conventional data association algorithms struggle with validation gate degradation, covariance divergence, and combinatorial explosion in these scenarios.

Purpose of the Study:

• To propose a time-adaptive fast association method for multi-target tracking using sparse, discontinuous observations from LEO satellite constellations.
• To enhance the accuracy and completeness of surface maritime target detection.

Main Methods:

• Developed a multi-target, time-adaptive fast association method within the Joint Probabilistic Data Association (JPDA) framework.
• Implemented a time-interval adaptive gating mechanism to maintain detection probability across varying revisit times.
• Introduced a progressive clustering strategy inspired by simulated annealing to decompose large association graphs.
• Integrated Depth-First Search (DFS) heap pruning with the Hungarian algorithm for numerical robustness.

Main Results:

• The proposed method significantly improves tracking accuracy and track completeness compared to conventional algorithms.
• Demonstrated enhanced performance across diverse constellation coverage and maritime clutter intensities.
• Achieved execution efficiency suitable for real-time simulation and engineering applications.

Conclusions:

• The developed method effectively addresses the limitations of traditional data association in LEO satellite remote sensing for maritime target detection.
• The approach offers a robust and efficient solution for enhancing surface maritime surveillance capabilities.