@inproceedings{ed38d88ac8104dc291a6510441554dc4,
title = "Matrix Reconstruction to Estimate Direction of Arrival of Coherent Sources Based on Planar Array",
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. ",
keywords = "Coherent sources, Direction of arrival, Matrix reconstruction, Minimum redundant linear arrays, Planar array",
author = "Hao Zhang and Dong Zhen and Fang Zeng and Guojin Feng and Zhaozong Meng and Fengshou Gu",
note = "Funding Information: ACKNOWLEDGMENT The work was supported by the National Natural Science Foundation of China under Grant No. 51875166.The support is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Prognostics and Health Management Conference, PHM-London 2022 ; Conference date: 27-05-2022 Through 29-05-2022",
year = "2022",
month = jul,
day = "1",
doi = "10.1109/PHM2022-London52454.2022.00030",
language = "English",
isbn = "9781665479554",
series = "Proceedings - 2022 Prognostics and Health Management Conference, PHM-London 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "127--131",
editor = "Chuan Li and Gianluca Valentino and Ling Kang and Diego Cabrera and Dejan Gjorgjevikj",
booktitle = "Proceedings - 2022 Prognostics and Health Management Conference, PHM-London 2022",
address = "United States",
}