Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (12): 3568-3576.doi: 10.13229/j.cnki.jdxbgxb.20230103

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Direction of arrival estimation based on improved orthogonal matching pursuit algorithm

Hui-jing DOU(),Dong-xu XIE,Wei GUO,Lu-yang XING   

  1. Department of Information Science，Beijing University of Technology，Beijing 100124，China
  • Received:2023-02-06 Online:2024-12-01 Published:2025-01-24

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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

CLC Number: 

  • TN911.7

Fig.1

Schematic diagram of the array receiving signal"

Fig.2

Convergence factor a curve"

Table 1

Three different algorithms are the best results for the test function"

函数名PSOGWOSGWO
均值标准差均值标准差均值标准差
Sphere-5.200 3e-261.190 6e-25-1.542 6e-984.178 3e-98-1.949e-1475.635 9e-119
Schwefel 2.22-2.618 8e-137.847 2e-13-1.695 7e-552.198 7e-55-1.042 9e-811.896 7e-81
Schwefel 1.2-3.707 3e-065.490 7e-06-3.091 7e-459.186 1e-45-1.039 1e-893.192 7e-89
Schwefel 2.21-5.117e-064.08e-06-9.430 1e-361.508 2e-35-5.746 7e-597.122 1e-59
Rastrigin-6.168 72.335 70000
Griewank-0.091 6880.032 499-0.006 962 60.016 17200
Ackley-1.258 5e-123.769 6e-12-4.085 6e-151.123 5e-15-3.730 3e-150
Hartman-3.862 80.000 253 9-3.861 80.002 455 7-3.859 92.269 1e-15

Fig.3

Convergence curves of each algorithm under different test functions"

Fig.4

DOA estimation of coherent signal sources by each algorithm"

Fig.5

Average running time of the algorithm varies with sparsity"

Fig.6

Success rate of the algorithm changes with the signal-to-noise ratio"

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