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DOA Estimation Based on Pseudo-Noise Subspace for Relocating Enhanced Nested Array
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DOI：10.1109/LSP.2022.3199149.png)
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摘要
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In this letter, a novel relocating enhanced nested array (RENA) configuration is proposed. Compared with most existing sparse array configurations, the proposed RENA has a hole-free difference co-array, simple closed expressions for the array geometry and degrees of freedom (DOFs), and also achieves more consecutive DOFs. Based on the above good properties of the proposed RENA, we improve a root multi-signal classification algorithm based on pseudo-noise subspace (PNS-root-MUSIC) for direction of arrival (DOA) estimation. The PNS-root-MUSIC algorithm has lower algorithm complexity due to no exhaustive spectral peak search, and takes full advantage of the larger hole-free co-array of the proposed RENA, yielding a higher accuracy of DOA estimation. The results of theoretical analysis and simulations demonstrate the superior performance of the proposed RENA. The simulation results show that the improved PNS-root-MUSIC algorithm has better DOA estimation performance compared with that of existing algorithms.
Keyword:
DOA estimation
degrees of freedom
hole-free co-array
pseudo-noise subspace
relocating enhanced nested array
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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