基于分层人工鱼群的相干极化-DOA联合估计  

Polarization-DOA joint estimation of coherent signal sources based on hierarchical artificial fish swarm algorithm

作　　者：曹丙霞[1] 刘威 李享 闫锋刚 金铭[1] CAO Bingxia;LIU Wei;LI Xiang;YAN Fenggang;JIN Ming(School of Information Science and Engineering,Harbin Institute of Technology(Weihai),Weihai 264209,China)

机构地区：[1]哈尔滨工业大学(威海)信息科学与工程学院,威海264209

出　　处：《遥测遥控》2023年第1期88-98,共11页Journal of Telemetry,Tracking and Command

基　　金：国家自然科学基金(61971158,61871149);山东省自然科学基金(ZR2020YQ46)。

摘　　要：智能优化算法是解决多维非线性优化问题、提高计算效率的有力工具。本文针对相干辐射源极化-空间角联合估计中计算量巨大的工程难题,以广义子空间拟合约束公式为代价函数,提出一种分层人工鱼群算法。该算法基于分层协同策略将鱼群分为底层和顶层,底层以人工鱼群算法进行全局搜索以保证种群多样性,顶层以粒子群算法进行局部搜索以加快收敛速度。仿真结果证明:分层人工鱼群算法能大幅降低广义子空间拟合的计算量,尤其是在较多目标的情况下。算法可有效提高计算效率,同时可提供优于传统人工鱼群算法的估计精度。Intelligent optimization algorithms are powerful tools to solve multi-dimensional nonlinear optimization problems and improve computational efficiency.A hierarchical artificial fish swarm algorithm is proposed in this paper,taking generalized subspace fitting constraint formula as the cost function.The proposed algorithm aims at the difficult engineering problem of huge computation in coherent radiation sources polarization-spatial angle joint estimation.The algorithm divides the fish swarm into a bottom layer and a top layer based on a hierarchical collaborative strategy.The bottom layer uses artificial fish swarms for global search to ensure population diversity.Meanwhile,the top layer adopts particle swarms for local search to speed up the convergence rate.The simulation results show that the hierarchical artificial fish swarm algorithm can greatly reduce the calculation amount of generalized subspace fitting,especially in the case of more targets.The algorithm can effectively improve computational efficiency and also provide better estimation accuracy than traditional artificial fish swarm algorithm.

关 键 词：波达方向估计 极化敏感阵列 智能优化算法 子空间拟合 相干信号 

分 类 号：TN957.51[电子电信—信号与信息处理] TN959.2[电子电信—信息与通信工程]