基于改进的正交匹配跟踪算法的波达方向估计

doi:10.13229/j.cnki.jdxbgxb.20230103

摘要/Abstract

摘要：

针对正交匹配跟踪算法在估计精度要求高时计算量较大且无法剔除错误原子的问题，提出了一种基于改进灰狼算法优化的正交匹配跟踪算法。该算法首先在灰狼算法的基础上进行改进，提出了一种基于Sigmoid函数演化而来的非线性收敛因子，并在灰狼算法的位置更新策略中引入动态权重。然后，将改进的灰狼算法应用到压缩感知的波达方向估计领域，利用改进的灰狼算法优化正交匹配跟踪算法的原子匹配过程，以减少正交匹配跟踪算法的计算量和运行时间，同时在正交匹配跟踪算法中引入回溯思想提高算法正确率。最后，通过仿真实验证明本文算法相较原算法具有估计精度高、运行速度快、抗噪能力强等优点。

关键词: 信号与信息处理, 波达方向估计, 压缩感知, 稀疏重构, 正交匹配跟踪, 灰狼算法

Abstract:

To address the problem of high computational complexity and inability to remove incorrect atoms in the orthogonal matching pursuit （OMP） algorithm when high estimation accuracy is required， an improved OMP algorithm based on the improved grey wolf optimization algorithm is proposed. Firstly， a nonlinear convergence factor based on the sigmoid function is proposed to improve the grey wolf algorithm， and a dynamic weighting method is introduced into the position update strategy of the grey wolf algorithm. Then， the improved grey wolf algorithm is applied to the field of compressed sensing DOA estimation， and the atom matching process of the OMP algorithm is optimized by using the improved grey wolf algorithm， which reduces the computational complexity and running time of the OMP algorithm， and introduces a backtracking thought to improve the correctness of the algorithm. Finally， simulation experiments demonstrate that the proposed algorithm has higher estimation accuracy， faster operation speed， and stronger anti-noise ability compared to the original algorithm.

Key words: signal and information processing, direction of arrival （DOA） estimation, compressive sensing, sparse refactoring, orthogonal matching pursuit, grey wolf optimization algorithm

中图分类号:

TN911.7

窦慧晶,谢东旭,郭威,邢路阳. 基于改进的正交匹配跟踪算法的波达方向估计[J]. 吉林大学学报(工学版), 2024, 54(12): 3568-3576.

Hui-jing DOU,Dong-xu XIE,Wei GUO,Lu-yang XING. Direction of arrival estimation based on improved orthogonal matching pursuit algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3568-3576.

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函数名	PSO		GWO		SGWO
函数名	均值	标准差	均值	标准差	均值	标准差
Sphere	-5.200 3e-26	1.190 6e-25	-1.542 6e-98	4.178 3e-98	-1.949e-147	5.635 9e-119
Schwefel 2.22	-2.618 8e-13	7.847 2e-13	-1.695 7e-55	2.198 7e-55	-1.042 9e-81	1.896 7e-81
Schwefel 1.2	-3.707 3e-06	5.490 7e-06	-3.091 7e-45	9.186 1e-45	-1.039 1e-89	3.192 7e-89
Schwefel 2.21	-5.117e-06	4.08e-06	-9.430 1e-36	1.508 2e-35	-5.746 7e-59	7.122 1e-59
Rastrigin	-6.168 7	2.335 7	0	0	0	0
Griewank	-0.091 688	0.032 499	-0.006 962 6	0.016 172	0	0
Ackley	-1.258 5e-12	3.769 6e-12	-4.085 6e-15	1.123 5e-15	-3.730 3e-15	0
Hartman	-3.862 8	0.000 253 9	-3.861 8	0.002 455 7	-3.859 9	2.269 1e-15