# An Enhanced MOPSO Method for Distributed Radar Topology Optimization

## TL;DR

This paper introduces a new radar node layout optimization method that improves positioning accuracy and coverage using an enhanced multi-objective optimization algorithm.

## Contribution

The novel contribution is an improved NS-MOPSO algorithm for distributed radar topology optimization with three objectives: GDOP minimization, coverage maximization, and geometric balance.

## Key findings

- The optimized radar layout reduces RMSPE by 6.4% compared to the best existing method.
- The new approach increases high-quality localization regions by 4.3%.
- The method shows faster convergence and improved stability in simulations and real-world experiments.

## Abstract

Time difference of arrival (TDOA) localization enables high-accuracy positioning by analyzing arrival-time differences of target signals at distributed radar nodes, whose performance strongly depends on radar node topology. However, existing studies tend to focus more on improving localization accuracy, while overlooking the impact of radar geometric layout and surveillance coverage on localization performance. To this end, this paper proposes a topology optimization method for a distributed radar system based on an improved non-dominated sorting multi-objective particle swarm optimization (NS-MOPSO) algorithm. A geometric localization model is developed for a distributed TDOA radar system. Based on this model, three optimization objectives are formulated, including minimizing geometric dilution of precision (GDOP), maximizing target coverage, and improving the geometric balance of node placement. These three objective functions are incorporated into the NS-MOPSO framework to achieve a more reasonable radar geometric distribution. To enhance the optimization performance, a series of strategies are adopted, such as non-dominated sorting for Pareto-based solution selection, an improved crowding-distance scheme to encourage balanced multi-objective optimization, and Gaussian mutation to increase solution diversity and reduce the risk of premature convergence. To validate the proposed method, both simulation studies and real-world experiments were conducted under different node deployment scenarios. The results show that the optimized topology achieves a 6.4% reduction in RMSPE and a 4.3% increase in the proportion of high-quality localization regions compared with the best-performing comparative method, while also demonstrating faster convergence and improved stability. These findings confirm the effectiveness and robustness of the proposed approach in enhancing localization accuracy, expanding effective coverage, and improving overall system performance.