Research on Multi-Launch Strategies for Smoke-Generating Decoy Bombs Based on Spatio-Temporal Collaborative Optimization Models

Abstract

Precision-guided weapons pose a severe threat to critical ground targets, making smoke screening a key technology for enhancing target survivability. Traditional smoke deployment methods suffer from limited flexibility and restricted coverage. Unmanned aerial vehicles (UAVs), with their high maneuverability, offer an ideal platform for precise smoke screening execution. This paper focuses on the spatio-temporal coordinated optimization problem for multi-deployment of smoke-generating decoys by UAVs. By establishing a unified spatio-temporal coordinate system, precise kinematic models are developed for the UAV, smoke-generating decoys, smoke clouds, and missiles. In a single-deployment scenario, an optimization model incorporating UAV flight parameters and decoy deployment timing parameters is formulated to maximize effective concealment duration. The particle swarm optimization algorithm is employed to derive the optimal deployment strategy through spatio-temporal coordination. To address multi-directional, multi-batch threats in combat scenarios, a multi-objective optimization model is extended. This model balances objectives including maximizing total effective concealment time, minimizing initial concealment time, and minimizing resource consumption. The NSGA-II algorithm is employed to obtain a set of Pareto optimal solutions. This research establishes a comprehensive technical approach from theoretical modeling to algorithmic solution. Simulation validation confirms the model's validity and the algorithm's effectiveness, providing theoretical foundations and algorithmic support for the practical application of UAV-mounted smoke decoy systems.