Research on the Optimization of UAV Smoke Screen Interference Bomb Delivery Strategy Based on Spatio - Temporal Motion Model

Abstract

This paper aims to optimize the coordinated masking tactics of UAV smoke screen interference bombs to maximize the effective masking time against incoming missiles. The research focuses on the strategy deployment problem of UAVs delivering smoke screen interference bombs and gradually constructs and solves a series of mathematical models. First, for the single - UAV - single - bomb scenario under fixed parameters, a spatio - temporal motion trajectory model of the missile, the UAV, and the smoke screen cloud is established. Through the line - of - sight blocking principle, an effective masking duration of 1.38 seconds is calculated, which lays a benchmark for subsequent optimizations. On this basis, the problem is transformed into a multi - dimensional nonlinear optimization problem. The flight angle, speed of the UAV, and the delivery and detonation times of the interference bombs are taken as decision variables, aiming to maximize the effective masking time. By using the genetic algorithm to conduct a global parameter scan under multiple physical and tactical constraints, the effective masking time is significantly increased to 4.58 seconds. Subsequently, to address the complexity of the coordinated delivery of multiple bombs (three interference bombs) by a single UAV, the optimization model framework is extended. More decision variables are introduced, and under the delivery interval constraints, the particle swarm optimization (PSO) algorithm is used to efficiently solve the high - dimensional non - convex problem. Finally, an effective masking duration of a total of 6.25 seconds is achieved.