A Study on Deployment Strategies Optimization of Distributed Radar for TDOA-based Localization

This paper addresses the optimization of deployment strategies for distributed radar systems in time difference of arrival localization tasks, aiming to enhance both localization and surveillance performance. Existing research typically focuses solely on optimizing radar node positions while neglecting the coupling between node orientation and surveillance performance. This paper introduces a joint optimization strategy for node placement and orientation, establishing a framework that addresses both single-objective localization optimization and multi-objective optimization, effectively balancing localization and surveillance tasks. The complex coupled constraints and non-convex nature of these optimization problems make analytical solutions difficult. Therefore, the paper also proposes a region-constrained multi-objective particle swarm optimization (RC-MOPSO) algorithm to find the optimal deployment strategy. This algorithm ensures that particles satisfy the complex constraints throughout the iterative process by introducing regional constraints during initialization and updates. Simulation results demonstrate that the proposed strategy achieves an optimal balance between localization and surveillance performance, significantly outperforming random deployment schemes, and exhibiting strong robustness to errors in radiation source power estimation.