Off-Grid DOA Estimation Based on Analysis of the Convexity of Maximum Likelihood Function

Liang LIU  Ping WEI  Hong Shu LIAO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E98-A   No.12   pp.2705-2708
Publication Date: 2015/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E98.A.2705
Type of Manuscript: LETTER
Category: Digital Signal Processing
compressive sensing,  direction-of-arrival estimation,  off-grid model,  convexity,  deterministic maximum likelihood,  

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Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation, owing to its advantages over conventional versions. However the performance of compressive sensing (CS)-based estimation methods degrades when the true DOAs are not exactly on the discretized sampling grid. We solve the off-grid DOA estimation problem using the deterministic maximum likelihood (DML) estimation method. In this letter, on the basis of the convexity of the DML function, we propose a computationally efficient algorithm framework for off-grid DOA estimation. Numerical experiments demonstrate the superior performance of the proposed methods in terms of accuracy, robustness and speed.