A Sparse Recovery Method for DOA Estimation Based on the Sample Covariance Vectors

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• 作者：Xiaorong Jing ; Xuefeng Liu ; Hongqing Liu
• 关键词：Array signal processing ; Sparse recovery ; DOA estimation ; Covariance vectors
• 刊名：Circuits, Systems, and Signal Processing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：36
• 期：3
• 页码：1066-1084
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Electronics and Microelectronics, Instrumentation;
• 出版者：Springer US
• ISSN：1531-5878
• 卷排序：36

文摘

In this paper, a computation-efficient method utilizing sparse recovery technique is proposed to address the problem of direction of arrival (DOA) estimation based on sample covariance matrix vectors. In the development of the new method, the DOA estimation problem is reformulated in a way that each column of the sample covariance matrix is reintroduced as pseudo-measurements. With this reformulation, multiple candidates of the DOA estimation are obtained by utilizing sparse recovery concept in which an explicit formula of the threshold parameter is provided. The optimal DOA estimation is then selected by employing the maximum likelihood estimation criterion from these multiple candidates. The proposed approach not only has higher resolution and ability of processing coherent sources without the need of decorrelation preprocessing, but also exhibits robust performance, especially in the case of low signal-to-noise ratio and/or small number of snapshots. Numerical studies confirm the effectiveness of the proposed method.