DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays.

Jianfeng Li, Feng Wang, Defu Jiang

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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3 authors.

Jianfeng LiArray and information processing laboratory, College of computer and information, Hohai University, Nanjing 211100, China. lijianfeng@hhu.edu.cn.

Feng WangArray and information processing laboratory, College of computer and information, Hohai University, Nanjing 211100, China. wangfeng@hhu.edu.cn.

Defu JiangArray and information processing laboratory, College of computer and information, Hohai University, Nanjing 211100, China. jiangdefu@hhu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A fast direction of arrival (DOA) estimation method using a real-valued cross-correlation matrix (CCM) of coprime subarrays is proposed. Firstly, real-valued CCM with extended aperture is constructed to obtain the signal subspaces corresponding to the two subarrays. By analysing the relationship between the two subspaces, DOA estimations from the two subarrays are simultaneously obtained with automatic pairing. Finally, unique DOA is determined based on the common results from the two subarrays. Compared to partial spectral search (PSS) method and estimation of signal parameter via rotational invariance (ESPRIT) based method for coprime arrays, the proposed algorithm has lower complexity but achieves better DOA estimation performance and handles more sources. Simulation results verify the effectiveness of the approach.

Indexed as

coprime arrayscross-correlation matrixdirection of arrival estimationpair matching

Identifiers

PMID28335536

DOI10.3390/s17030638

PMCPMC5375924

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