基于最优邻域搜索改进模拟退火的多雷达优化布站

Multi-radar deployment based on improved simulated annealing with optimal neighborhood search

针对多雷达组网探测系统,首先建立以空域覆盖率为优化目标、以多雷达位置为优化变量的数学模型,将多雷达布站建模为一个离散优化问题,从而筹划形成最优的多雷达部署方案.其次提出一种基于最优邻域搜索的改进模拟退火算法,通过在历史全局最优解的邻域范围内搜索产生新解来提升算法收敛速度;为确保算法的有效性,利用多项复杂性能测试函数对改进算法进行全面的性能分析.最后,在典型的仿真场景中,设定6部雷达、2个高度层的环境条件,对提出的算法进行验证.仿真结果表明,基于最优邻域搜索的改进模拟退火算法在收敛速度上表现优异,且以此为基础得到的多雷达布站方案能够满足任务需求,确保空域覆盖率的最大化.

A mathematical model with airspace coverage as the optimization objective and multiple radar positions as the optimization variables was first established for the multi-radar network detection system,modeling the deployment process as a discrete optimization problem to obtain the optimal deployment plan.Secondly,an improved simulated annealing algorithm based on optimal neighborhood search was proposed,which improved the convergence speed of the algorithm by searching within the neighborhood range of historical global optimal solutions to generate new solutions.To ensure the effectiveness of the algorithm,a comprehensive performance analysis of the improved algorithm was conducted using multiple complex performance testing functions.Finally,the validation of the proposed method was conducted in a typical scenario of 6 radars and 2 altitude layers.Simulation results show that the improved simulated annealing algorithm can effectively accelerate convergence speed,and the multi-radar deployment scheme can meet the task requirements and ensure the maximization of airspace coverage.