Configuration strategy for unmanned surface vehicle coordinated defense system based on particle swarm optimization

Aiming at the cooperative defense deployment problem for maritime Unmanned Surface Vessels (USVs), this paper proposes an intelligent configuration strategy based on the Particle Swarm Optimization (PSO) algorithm. By establishing a detection model for the defense units and a kinematic model for the attack, the strategy optimizes defense deployment positions under varying attack speeds aiming to maximize both the area and perimeter coverage of the maximum fire range circle. This paper innovatively introduces a joint optimization metric that integrates area and perimeter coverage, which can theoretically be expressed as a weighted combination of the Area Coverage Rate (ACR) and the Perimeter Coverage Rate (PCR). Building upon this, a dynamic model is constructed where the detection radius varies with the speed of the attacking USV, and a PSO-based deployment optimization method combined with a parallel computing framework is designed. The study establishes multiple optimization modes and systematically analyzes the impact of the attacking USV's speed on the detection radius and coverage effectiveness. Simultaneously, Monte Carlo random search, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) are introduced as comparison methods to evaluate the coverage performance and computational efficiency of the PSO-based deployment optimization method. The simulation results show that, under the same distance–azimuth joint optimization framework, the PSO-based configuration strategy achieves competitive coverage performance, while generally requiring the shortest computational time and simpler implementation.Thus, PSO offers a favorable balance among solution quality, efficiency, and practical applicability, providing an effective approach for coordinated defensive deployment in complex maritime environments.