融合PSO和Powell的雷达组网反隐身部署优化算法

PSO-Powell-Integrated Algorithm for Anti-stealth Deployment Optimization of Netted Radar

针对多雷达组网探测隐身目标的部署优化问题,根据雷达探测隐身目标的简化模型,在目标运动轨迹确定的情况下,设计了反隐身部署优化两级指标。由于雷达网部署为具有多个可行解的多目标优化问题,提出了一种融合粒子群(Particle swarm optimization,PSO)和鲍威尔(Powell)搜索法的分层搜索算法。首先采用粒子群优化算法得到全局和局部最优解,然后采用鲍威尔算法进一步搜索得到部署方案。仿真结果表明,提出的算法充分结合了粒子群算法的全局搜索能力和鲍威尔算法的局部搜索能力,与仅采用粒子群算法相比,得到的部署方案在保证责任区覆盖的前提下,有效提高了雷达网对隐身目标的探测概率,增加了对隐身目标的预警距离。

Abstract： To solve the deployment optimization problem for detecting stealth target based on netted ra- dar, two-level anti-stealth optimization indexes are presented according to the simplified model of detec- ting stealth target using single radar when the object trajectory is fixed. Since netted radar deployment is a multi-objective optimization problem with multiple solutions, the hierarchical search algorithm integrat- ed particle swarm optimization （PSO） with Powell is proposed. Firstly PSO algorithm is used to obtain global and local optimal solution. Then the Powell algorithm is used to search the final deployment solu- tion. Simulation results demonstrate that the proposed algorithm combines the advantages of PSO＇s glob- al search ability and Powell＇s local search ability. The deployment solution using the proposed algorithm compared with the PSO algorithm improves the detection probability of stealth target effectively, and in- creases the warning distance, under the premise that responsible cover area does not decrease.