Geometric Optimization of Distributed Radar System for TDOA-based Jamming Source Localization

Accurate localization of jamming sources is essential for developing anti-jamming strategies. This paper proposes a geometric optimization method for distributed radar systems, aimed at enhancing both localization and surveillance performance in jamming source localization based on time difference of arrival measurements. First, evaluation metrics for localization and surveillance performance are established, leading to the formulation of a constrained multi-objective optimization problem. To address the limitations of existing algorithms that often converge to local optima, this paper introduces a randomized relaxation-based multi-objective particle swarm optimization algorithm. This algorithm effectively balances the sensitivity differences between different optimization metrics by designing new individual best particle update rules, significantly enhancing the search capability. Simulation results demonstrate that the proposed algorithm excels in handling objective functions with optimization sensitivity differences, and the solution sets obtained show significant advantages in both localization and surveillance performance compared to traditional algorithms. Additionally, this algorithm shows strong robustness against variations in jamming source transmission power, indicating its practical application potential.