一种多基地浮标阵位优化方法
Multistatic Buoy Array Optimization Algorithm
秦瑞廷 1赵云 2蔡清裕 3孙海洋1
作者信息
- 1. 河南科技大学,河南 洛阳 471000
- 2. 国防科技大学,湖南 长沙 410000
- 3. 河南科技大学,河南 洛阳 471000;湖南三一工学院,湖南 长沙 410000
- 折叠
摘要
针对水域监视中多基地浮标发射、接收节点阵位布置问题,提出了一种基于遗传粒子群算法的阵位优化方法.基于Argo浮标数据和ETOPO1 地形数据计算得到真实海洋环境下的传播损失,结合双基地声纳方程和概率融合建立起多基地浮标阵性能评估模型.采用粒子群算法、遗传算法和遗传粒子群算法对该模型进行优化.仿真结果表明,经过优化后的多基地浮标阵,其覆盖能力与传统布阵方案相比其探测能力有明显的提高,且遗传粒子群算法在搜索能力和收敛速度上更为均衡,达到了优化布防的目的.
Abstract
According to the problem of transmitter and receiver node arrangement of multistatic buoy in sea area surveillance,this paper presents an array optimization method based on GAPSO(genetic algorithm-particle swarm optimization).Firstly,based on Argo buoy data and ETOPO1 topographic data,the transmission loss in the real marine environment was calculated,and then the performance evaluation model of multistatic buoy array was estab-lished by probability fusion based on the bistatic sonars equation.Secondly,we took the effective coverage rate of the model as the objective function,and then particle swarm optimization(PSO),genetic algorithm(GA)and GAPSO were used to optimize the array.The simulation results show that the coverage ability of the optimized multistatic buoy array is significantly improved compared with the traditional array scheme.Compared with PSO and GA,the GAPSO has obvious improvement in search ability and convergence speed,which achieves the purpose of optimal defense de-ployment.
关键词
多基地浮标/搜索效能评估/融合的群智能算法/阵位优化Key words
Multistatic buoy/Searching efficiency evaluation/Fusion swarm intelligence algorithm/Array optimiza-tion引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据