Two-dimensional DOA estimation for coherent sources based on reduction dimension sparse reconstruction

被引：0
|
作者：
Wang X.-H. [1 ,2 ]
Mao X.-P. [1 ,3 ]
Zhang N.-T. [1 ]
机构：
[1] School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin
[2] School of Information and Electrical Engineering, Harbin Institute of Technology(Weihai), Weihai
[3] Collaborative Innovation Center of Information Sensing and Understanding at Harbin Institute of Technology, Harbin
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2016年 / 38卷 / 08期
关键词：
Coherent sources; Direction of arrival (DOA); Reduction dimension sparse reconstruction (RDSR); Redundant sub-dictionary; Two-dimensional (2-D) DOA estimation;
D O I：
10.3969/j.issn.1001-506X.2016.08.01
中图分类号：
学科分类号：
摘要：
The problem of high computational complexity will be caused if compressed sensing (CS) is directly applied to two-dimensional (2-D) direction of arrival (DOA) Estimation of coherent sources. To solve this problem, a 2-D DOA estimation method based on reduction dimension sparse reconstruction (RDSR) is proposed. The proposed method converts the construction of a 2-D redundancy dictionary into that of a 1-D dictionary by using the special array structure. In addition, a pair-matching scheme is proposed based on spatial spectrum reconstruction of the sub-dictionary. Therefore, the proposed method not only reduces the computational complexity but also improves the pairing probability of success. Simulation results show that the estimated performance of the method is close to the Cramér-Rao lower bound (CRLB), even in the case of low signal-to-noise ratio (SNR), small number of snapshots and small angle interval, the estimation performance is still good. © 2016, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1709 / 1715
页数：6
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