Optimization Of Collaborative UAV Smoke Screen Deployment for Missile Defense Based on Intelligent Optimization
DOI:
https://doi.org/10.54097/3z484g83Keywords:
UAV Swarm, Missile Defense, Multi-objective Optimization, Kinematic Modeling.Abstract
The increasing threat of precision-guided missiles to high-value assets necessitates the development of effective, dynamic countermeasures. Smoke screens, deployed by Unmanned Aerial Vehicles (UAVs), offer a flexible, low-cost, and highly effective means of obscuring targets from optical and infrared seekers. However, maximizing their defensive capability requires sophisticated spatiotemporal coordination. To address this challenge, this paper proposes a comprehensive optimization model for the intelligent deployment of UAV-launched smoke screens. This study establish a "Kinematics-Geometry-Optimization" framework, which integrates detailed mathematical models for the trajectories of UAVs, missiles, and descending smoke clouds. Intelligent optimization algorithms, specifically Particle Swarm Optimization (PSO) and Simulated Annealing (SA), are employed to solve the high-dimensional problem of maximizing the total effective obscuration time across various combat scenarios. Simulations validate the model's efficacy, demonstrating a 213% increase in obscuration time in a single-asset scenario. In a complex engagement involving 5 UAVs defending against 3 missiles, the model successfully coordinates the deployment of 13 smoke bombs to achieve a total obscuration time of 100.22 seconds, forming a seamless, multi-layered defensive barrier. The proposed model provides a systematic, scalable, and computationally efficient solution for real-time tactical planning in missile defense.
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