Battlefield Target Cooperative Deployment Algorithm Based on Improved PSO Multi-Sensor

DOI: https://doi.org/10.62517/jbdc.202601112

Author(s)

Chenxuan Wang, Qiuchun Jin

Affiliation(s)

Zhengzhou University of Aeronautics, Zhengzhou, China

Abstract

In modern battlefield environments, precise target monitoring in complex terrains faces challenges of insufficient coverage and low resource utilization efficiency with traditional multi-sensor deployment strategies. This paper proposes an improved Particle Swarm Optimization (PSO)-based multi-sensor cooperative deployment algorithm for battlefield target monitoring, aiming to enhance the overall effectiveness of monitoring systems through intelligent optimization and cooperative sensing. First, a multi-sensor joint detection probability model is constructed, establishing a statistical sensing model incorporating parameters such as sensing radius and adjustment coefficients. Second, a PSO algorithm improved with adaptive inertia weight is introduced, enabling global optimization of sensor positions by dynamically adjusting particle search strategies. Finally, a priority coverage strategy for key areas is designed to ensure 100% monitoring coverage in critical target regions. Simulation experiments demonstrate that compared with random deployment algorithms and standard PSO algorithms, the proposed algorithm increases the effective coverage of monitoring areas from 51.51% to 66.44%, maintains stable 100% coverage in key areas, and significantly enhances target monitoring capabilities and resource utilization efficiency in complex battlefield environments under the same resource conditions, providing an effective solution for multi-sensor cooperative deployment.

Keywords

Multi-Sensor Cooperative Deployment; Battlefield Target Monitoring; Improved Particle Swarm Optimization (PSO)

References

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