DOA Estimations With Limited Snapshots Based on Improved Rank-One Correlation Model in Unknown Nonuniform Noise

It is well known that classic direction-of-arrival (DOA) estimation techniques yield unsatisfactory performance with limited snapshots in unknown nonuniform noise. In this article, a sparse reconstruction (SR) DOA estimation method combining vectorized and reduced signal covariance matrix (SCM) is p...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2021-10, Vol.70 (10), p.10308-10319
Main Authors: Fang, Yunfei, Zhu, Shengqi, Zeng, Cao, Gao, Yongchan, Li, Shidong
Format: Article
Language:English
Subjects:
Algorithms
alternating grid optimization (AGO)
Arrays
Computational modeling
Correlation
Covariance matrices
Covariance matrix
Direction of arrival
Direction of arrival (DOA)
Direction-of-arrival estimation
Estimation
Mathematical analysis
matrix completion (MC)
Noise
Noise reduction
nonuniform noise
Optimization
Robustness (mathematics)
sparse reconstruction (SR)
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Summary:It is well known that classic direction-of-arrival (DOA) estimation techniques yield unsatisfactory performance with limited snapshots in unknown nonuniform noise. In this article, a sparse reconstruction (SR) DOA estimation method combining vectorized and reduced signal covariance matrix (SCM) is proposed. The new approach is based on an improved rank-one correlation model for denoising. With an extended virtual aperture array, this technique is able to provide a high-resolution DOA estimation with limited snapshots and is robust to nonuniform noise. In addition, two effective algorithms are also derived to obtain noise-free SCMs. One is to obtain diagonal entries of the noise-free SCM from the off-diagonal entries based on the fact that diagonal entries of SCM across the array are identical. The other is based on matrix completion (MC) theory which uses the low rank property of the SCM. Numerical results demonstrate that the new approaches yield superior performance than existing techniques in DOA estimations for multiple and compactly placed sources.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2021.3105673