Matrix Reconstruction to Estimate Direction of Arrival of Coherent Sources Based on Planar Array

Hao Zhang, Dong Zhen, Fang Zeng, Guojin Feng, Zhaozong Meng, Fengshou Gu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Multiple Signal Classification (MUSIC) algorithm has become a landmark algorithm in the theoretical system of spatial spectrum estimation. This technology has excellent estimation performance and wide application prospects. Accurate Direction of Arrival (DOA) estimation plays a pivotal role in the detection of narrow wave sources. Nevertheless, when the signals are partially correlated or even coherent, the performance of the traditional MUSIC algorithm is greatly reduced. Methods such as spatial smoothing and Toeplitz matrix reconstruction have been proposed to decoherence and minimize the DOA estimation error in the MUSIC algorithm. However, these methods can only be applied to uniform linear arrays, which greatly reduces the practicability of the algorithm. This paper proposes to combine a decoherence method with MUSIC algorithm to estimate the azimuth angle (θ) and elevation angle (ϕ) of the source in a planar array which is composed of two orthogonal minimum redundant linear arrays (MRLA). The algorithm is implemented under different Signal-to-Noise Ratio (SNR) and compared with other decoherence methods. Simulation results show the proposed decoherence algorithm can achieve higher DOA estimation accuracy for coherent sources.

Original languageEnglish
Title of host publicationProceedings - 2022 Prognostics and Health Management Conference, PHM-London 2022
EditorsChuan Li, Gianluca Valentino, Ling Kang, Diego Cabrera, Dejan Gjorgjevikj
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages127-131
Number of pages5
ISBN (Electronic)9781665479547
ISBN (Print)9781665479554
DOIs
Publication statusPublished - 1 Jul 2022
Event2022 Prognostics and Health Management Conference - London, United Kingdom
Duration: 27 May 202229 May 2022

Publication series

NameProceedings - 2022 Prognostics and Health Management Conference, PHM-London 2022
PublisherIEEE
ISSN (Print)2166-563X
ISSN (Electronic)2166-5656

Conference

Conference2022 Prognostics and Health Management Conference
Abbreviated titlePHM-London 2022
Country/TerritoryUnited Kingdom
CityLondon
Period27/05/2229/05/22

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