基于几何序列分解与稀疏重构的DOA估计

doi:10.11959/j.issn.1000-436x.2023011

Abstract

Abstract:

In order to solve the problem of direction finding of coherent signals for uniform circular array under the condition of underdetermination, a direction of arrival (DOA) estimation algorithm combining geometric sequence decomposition and sparse reconstruction was proposed.Geometric sequence decomposition was used to split coherent groups and estimate the actual direction vector of each coherent group, while sparse reconstruction was used to estimate DOA for each coherent group.Simulation results demonstrate that when the number of elements of the uniform circular array is M, compared with the existing algorithms, the maximum number of sources that can be estimated by the proposed algorithm is M(M-1).And when the number of sources is large, the success rate and accuracy of direction finding are better.In addition, the proposed algorithm can solve the “angle merger” problem, and has advantages in the direction finding tasks with very few snapshots.

Key words: geometric sequence decomposition, sparse reconstruction, condition of underdetermination, coherent signal, uniform circular array

CLC Number:

TN98

Jin HOU, Xinqiang CHEN. DOA estimation based on geometric sequence decomposition and sparse reconstruction[J]. Journal on Communications, 2023, 44(1): 153-163.

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References 21

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算法	DOA估计结果	最大误差	错误个数
目标DOA	[[32°,328°],[30°,210°],[89°,272°]]	—	—
OMP算法	[3°,325°,33°,213°,89°,273°]	3°	1
本文所提算法	[[32°,328°],[30°,210°],[89°,272°]]	0°	0

算法	DOA估计结果	最大误差
目标DOA	[[15°],[167°],[251°]]	—
OMP算法	[13°,169°,251°]	2°
MUSIC算法	[14°,164°,251°]	3°
DML算法	[13°,167°,250°]	2°
本文所提算法	[[15°],[167°],[251°]]	0°

DOA estimation based on geometric sequence decomposition and sparse reconstruction

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