Maximum Likelihood DOA Estimation Based on Improved Crawfish Optimization Algorithm

针对传统最大似然波达方向估计存在高计算量和低计算精度的缺点，文章提出了一种改进小龙虾优化算法(GSCOA)的最大似然估计算法。引入迁移机制、改进正弦算法和融合人工大猩猩优化器(GTO)增强了小龙虾算法的全局探索能力和局部开发能力，提升了小龙虾优化算法的收敛性能的同时也避免了陷入局部最优。实验结果表明，与基于小龙虾优化算法(COA)、粒子群优化算法(PSO)、蛇优化算法的最大似然估计算法相比，基于改进小龙虾优化算法的最大似然估计算法具有更强的收敛性能和更高的收敛精度。In response to the drawbacks of high computational complexity and low computational accuracy in traditional maximum likelihood direction of arrival estimation, this paper proposes an improved crayfish optimization algorithm (GSCOA) for maximum likelihood estimation. The introduction of a transfer mechanism, improved sine algorithm, and integrated artificial gorilla optimizer (GTO) have enhanced the global exploration and local development capabilities of the crayfish algorithm, improved the convergence performance of the crayfish optimization algorithm, and avoided falling into local optima. The experimental results show that compared with the maximum likelihood estimation algorithms based on the crayfish optimization algorithm (COA), particle swarm optimization algorithm (PSO), and snake optimization algorithm, the maximum likelihood estimation algorithm based on the improved crayfish optimization algorithm has stronger convergence performance and higher convergence accuracy.