Off-Grid Direction of Arrival Estimation Based on Joint Spatial Sparsity for Distributed Sparse Linear Arrays

Sensors

Yujie Liang¹  Rendong Ying²  Zhenqi Lu² 

[1] School of Electronic Information and Electrical Engineering, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China;

关键词: off-grid;    joint spatial sparsity;    distributed sparse linear arrays;    direction of arrival estimation;    concatenated atomic norm;    semidefine program;    distributed compressed sensing;   

DOI  :  10.3390/s141121981

来源: mdpi

【 摘 要 】

In the design phase of sensor arrays during array signal processing, the estimation performance and system cost are largely determined by array aperture size. In this article, we address the problem of joint direction-of-arrival (DOA) estimation with distributed sparse linear arrays (SLAs) and propose an off-grid synchronous approach based on distributed compressed sensing to obtain larger array aperture. We focus on the complex source distribution in the practical applications and classify the sources into common and innovation parts according to whether a signal of source can impinge on all the SLAs or a specific one. For each SLA, we construct a corresponding virtual uniform linear array (ULA) to create the relationship of random linear map between the signals respectively observed by these two arrays. The signal ensembles including the common/innovation sources for different SLAs are abstracted as a joint spatial sparsity model. And we use the minimization of concatenated atomic norm via semidefinite programming to solve the problem of joint DOA estimation. Joint calculation of the signals observed by all the SLAs exploits their redundancy caused by the common sources and decreases the requirement of array size. The numerical results illustrate the advantages of the proposed approach.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.