An Enhanced MOPSO Method for Distributed Radar Topology Optimization

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Optimization Algorithms

Background:

• Time Difference of Arrival (TDOA) localization offers high accuracy but is sensitive to radar node topology.
• Existing research often prioritizes localization accuracy over the impact of geometric layout and coverage.

Purpose of the Study:

• To propose a topology optimization method for distributed radar systems to enhance localization performance.
• To address the limitations of current TDOA localization by considering geometric layout and surveillance coverage.

Main Methods:

• Developed a geometric localization model for distributed TDOA radar systems.
• Formulated three optimization objectives: minimizing Geometric Dilution of Precision (GDOP), maximizing target coverage, and improving geometric balance.
• Employed an improved Non-Dominated Sorting Multi-Objective Particle Swarm Optimization (NS-MOPSO) algorithm with enhanced selection and diversity strategies.

Main Results:

• The optimized topology resulted in a 6.4% reduction in Root Mean Square Positional Error (RMSPE).
• Achieved a 4.3% increase in high-quality localization regions compared to existing methods.
• Demonstrated faster convergence, improved stability, and enhanced robustness in simulations and real-world experiments.

Conclusions:

• The proposed topology optimization method effectively enhances TDOA localization accuracy and expands surveillance coverage.
• The NS-MOPSO-based approach provides a robust and stable solution for distributed radar system design.
• Optimizing radar node geometry is crucial for maximizing system performance and achieving reliable positioning.