Sparse continuous-field and super-resolution method for direction-of-arrival estimation

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• 作者：Bo Lin ; Jubo Zhu
• 关键词：Direction ; of ; arrival (DOA) estimation ; Sparse continuous ; field ; Super ; resolution ; Covariance fitting criterion ; Sparsity constraint
• 刊名：Multidimensional Systems and Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：28
• 期：1
• 页码：329-340
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Artificial Intelligence (incl. Robotics);
• 出版者：Springer US
• ISSN：1573-0824
• 卷排序：28

文摘

In the application of direction-of-arrival (DOA) estimation based on sensor array, bearing-discretization has been a major technique for the sparsity-based methods in the past decade. However, discretization leads to the modeling error of sparse representation, which usually deteriorates the accuracy of DOA estimation. In this paper, a sparse continuous-field and super-resolution method (SCSM) is proposed to overcome the discretization issue. SCSM estimates DOA in the continuous bearing-space by solving a minimization problem, which combines the measurement covariance fitting criterion with the source-sparsity constraint characterized by minimizing an atomic norm. This minimization problem has a unique solution. After obtained the solution uniquely, Prony’s method is utilized to estimate the DOA of sources. Therefore, SCSM has the capability to acquire a sparse and accurate estimation of DOA. In addition, it has some other advantages, including excellent super-resolution capability, no requirement of source number, applicability to arbitrary number of snapshots. Numerical results demonstrate the superior performance of the presented method.