基于改进SP算法的多目标DOA估计方法

doi:10.12305/j.issn.1001-506X.2024.07.13

Abstract

Abstract:

The application of sparse reconstruction algorithms in radar target parameter estimation has been a hot topic in recent years. However, due to the limitations of sparse reconstruction algorithms, they are affected by the mutual influence between atoms when estimating the direction of arrival (DOA) of target waves, resulting in a decrease in the accuracy of multi-target angle measurement. To address this issue, a relaxation subspace tracking algorithm based on the idea of signal separation iteration is proposed. Firstly, the multiple atoms with the strongest correlation between the echo signal and the normalized dictionary matrix are calculated as the initial estimated values. Then, the initial estimated angles is used to construct the cost function, and estimate repeatedly until the cost function converges. The simulation results show that the proposed algorithm reduces the influence of the number of targets and phase difference, improves the angle measurement accuracy of multi-target DOA estimation, and reduces the computational complexity compared to traditional relaxation algorithms.

Key words: direction of arrival (DOA) estimation, sparse reconstruction, subspace pursuit, relaxation algorithm

CLC Number:

TN95

Ruoshi CAO, Yongbo ZHAO, Yucheng QIU. Multi-target DOA estimation method based on improved SP algorithm[J]. Systems Engineering and Electronics, 2024, 46(7): 2294-2300.

Figures/Tables 7

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Multi-target DOA estimation method based on improved SP algorithm

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