改进蚁群优化算法的最大似然DOA估计方法

西安工程大学电子信息学院,陕西西安710600

信号检测;参数估计;波达方向;最大似然估计;蚁群优化算法;精英反向学习;跨邻域搜索机制;计算复杂度

Maximum likelihood DOA estimation based on improved ant colony optimization algorithm
JIAO Yameng,LI Wenping,WU Yue,and CUI Lin

College of Electronic Information, Xi’an Polytechnic University, Xi’an 710600, Shannxi Province, P. R. China

signal detection; parameter estimation; direction of arrival; maximum likelihood estimation; ant colony op‐timization algorithm; elite reverse learning; across neighborhood search; computing complexity

DOI: 10.3724/SP.J.1249.2023.01033

备注

针对将连续域蚁群优化算法应用于最大似然(maximumlikelihood,ML)估计中存在计算量过大的问题,提出一种基于改进蚁群优化(modifiedantcolonyoptimization,MACO)算法的最大似然波达方向(maximumlikelihooddirectionofarrival,ML-DOA)估计方法.采用精英反向学习策略获得较优初始解群体,结合全局跨邻域搜索和高斯核函数局部搜索对蚁群的寻优方式进行优化,扩大了算法的搜索空间并加快了收敛速度,最终得到ML估计方法的非线性全局最优解.仿真结果表明,与基于粒子群优化(particleswarmoptimization,PSO)算法、蚁群优化(antcolonyoptimization,ACO)算法的ML估计方法相比,ML-MACO算法的收敛速度是ML-ACO算法的4倍,计算量是ML-ACO算法的1/3,分辨成功率高于ML-PSO算法和ML-ACO算法,估计误差小于ML-PSO算法和ML-ACO算法.ML-MACO算法以更低的计算量保持了ML算法的优良估计性能,收敛性能更优且估计精度更高.
In order to alleviate the huge amount of calculation when using the continuous domain ant colony optimization (ACO) algorithm to handle the maximum likelihood (ML) estimation problem, we propose a maximum likelihood direction of arrival (ML-DOA) estimation method based on modified ant colony optimization (MACO) algorithm. Firstly, MACO algorithm adopts the elite reverse learning strategy to obtain a better initial solution group. Secondly, the optimization method of ant colony is conducted by combining global cross-neighborhood search and local search of Gaussian kernel function to expand the algorithm search space and accelerate the convergence speed. Finally, the nonlinear global optimal solution of ML estimation method is obtained. Simulation results show that, compared with ML estimation methods based on particle swarm optimization (PSO) algorithm and ACO algorithm, the convergence speed of the ML-MACO algorithm is 4 times faster than that of the ML-ACO algorithm, the computational load is 1/3 that of the ML-ACO algorithm, the resolution success rate is higher than the ML-PSO algorithm and the ML-ACO algorithm, and the estimation error is less than the ML-PSO algorithm and the ML-ACO algorithm. The ML-MACO algorithm maintains the excellent estimation performance of the ML algorithm with lower computational effort, better convergence performance, and higher estimation accuracy.
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