声纳浮标阵目标搜索优化布放算法

Research on Sonobuoys Deployment in Searching Underwater Target

摘要:

为提高声纳浮标阵的目标搜索效率,文章首先建立目标运动模型和累积搜索概率的计算方法,然后提出了基于多点随机搜索、分区分支界定和遗传算法的浮标阵优化布放方法。仿真结果表明:这3种优化方法均优于传统布放方法,多点随机搜索算法性能最差但适合短时间制定布放策略；分区分支界定算法适合优化较少参数的浮标阵形,保证能找到最优点；对于较多的浮标阵形参数,利用遗传算法优化最好但运算时间较长。

Abstract:

It is important to optimize sonobuoys deployment for enhancing cumulative detection probability in aviation ASW. Firstly, the target motion model was established and method of calculation cumulative detection probability was interpreted. Then three kinds of global optimization algorithms were designed validly for sonobuoys deployment, based on multi-start random search, branch-and-bound based partition and a genetic algorithm. The simulation results showed that the multi-start random search algorithm was comparatively weak but fit for give deployment scheme in short time, branch-and-bound based partition algorithm could reach optimization but only fit few sonobouys array parameters, and GA was fit for sonobouys array with many parameters but need long time to calculate.