基于酉重构子空间的一维DOA估计

doi:10.3969/j.issn.0255-8297.2023.06.002

Abstract

Abstract: To address the limitations of traditional multiple signal classification (MUSIC) algorithm, such as ineffective performance in low signal to noise ratio (SNR), small snapshots and low array number under small incident angle interval signals, we propose an improved algorithm called unitary reconstructed subspace MUSIC (URS-MUSIC). The proposed algorithm transforms the actual received signal of a uniform linear array from complex to real value using unitary transformation, then reconstructs subspaces and revised matrices to obtain new spatial spectrums based on the size of the subspace eigenvectors. The obtained spectrums are multiplied by the signal subspace projection (SSP) to realize direction of arrival (DOA) estimation. Simulation results demonstrate that URS-MUSIC outperforms both traditional and signal subspace projection algorithms with better resolution performance, especially under challenging conditions such as low SNR, small snapshots, and low array numbers.

Key words: direction of arrival (DOA) estimation, unitary transform, reconstructive subspace, spatial spectrum estimation

CLC Number:

TN911.72

JIN Yanliang, Lü Rukun, WANG Xiaoyong, ZHENG Guoxin. One-Dimensional DOA Estimation Based on Unitary Reconstructive Subspace[J]. Journal of Applied Sciences, 2023, 41(6): 926-939.

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One-Dimensional DOA Estimation Based on Unitary Reconstructive Subspace

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