Cooperative Tracking of Vessel Trajectory by Multi-Static Passive Stations Using an MC-RMPF.

Area of Science:

• Maritime surveillance
• Radar signal processing
• Target tracking

Background:

• Traditional multi-static passive radar tracking methods suffer from high computational load and trajectory discontinuities due to varying data rates and noise.
• Existing methods struggle with estimation variance and position jumps between updates, impacting tracking reliability.

Purpose of the Study:

• To develop an efficient and accurate multi-station cooperative vessel tracking framework.
• To address computational overhead and trajectory discontinuity issues in passive radar tracking.
• To enhance the reliability of vessel tracking in challenging maritime environments.

Main Methods:

• A motion-constrained resample-move particle filter (MC-RMPF) framework is proposed.
• Systematic resampling alleviates particle degeneracy, while Markov Chain Monte Carlo (MCMC) rejuvenation ensures particle feasibility under motion constraints.
• A distributed detection network dynamically selects optimal observation subsets to balance accuracy and computational load.

Main Results:

• The MC-RMPF method significantly reduces Root Mean Square Error (RMSE) by 23.5% and Circular Error Probability (CEP) by 21.7% compared to baseline methods.
• The framework demonstrates robust performance in scenarios with target maneuvers and temporary observation loss.
• Improved localization accuracy and reduced computational burden were observed.

Conclusions:

• The proposed MC-RMPF framework offers a superior solution for multi-station passive radar vessel tracking.
• This method enhances tracking reliability and efficiency, particularly in dynamic maritime conditions.
• The cooperative framework effectively manages computational resources while maintaining high localization precision.