2D off‐grid DOA estimation using joint sparsity. Issue 9 (1st September 2019)

• Record Type: Journal Article
• Authors: Afkhaminia, Fatemeh; Azghani, Masoumeh
• Is Part Of: IET radar, sonar & navigation. Volume 13:Issue 9(2019)
• Page Start: 1580
• Page End: 1587
• Publication Date: 2019-09-01
• Subjects: array signal processing -- direction‐of‐arrival estimation -- estimation theory -- signal sources; 2D off‐grid DOA estimation technique -- direction of arrival estimation technique -- joint block sparsity property -- uniform rectangular array signal processing -- two‐dimensional DOA estimation -- steering matrix columns -- signal sources -- single snapshot technique; Signal processing -- Periodicals; Radar -- Periodicals; Sonar -- Periodicals; Electronics in navigation -- Periodicals; Navigation -- Periodicals; 621.3848
• DOI: 10.1049/iet-rsn.2018.5442
• Languages: English
• ISSNs: 1751-8784

Abstract

Abstract : Direction of arrival (DOA) estimation is an essential task in the array signal processing. In this study, the authors attempt to address the off‐grid issue for the two‐dimensional (2D) DOA estimation of a uniform rectangular array. To this end, they would offer a modelling for the 2D off‐grid problem based on joint sparsity. Leveraging the block sparsity property, they propose an algorithm to jointly recover the DOAs as well as the off‐grids. Moreover, they discuss that the smaller grid intervals would result in higher mutual correlation of the steering matrix columns which leads to the poor performance of the DOA estimation technique. On the other hand, large grid intervals would intensify the off‐grid issue. Therefore, to establish a compromise, they suggest choosing a slightly large grid interval for the DOA estimation problem and solving the off‐grid issue using the joint sparsity property. The simulation results confirm that the proposed method has better DOA estimation accuracy. A great advantage of the suggested DOA estimation scheme is that it is a single snapshot technique which does not require knowing the number of signal sources beforehand. Moreover, they have observed that the suggested scheme is more robust against noise and the large number of source signals.