Off-grid DOA Estimation for Colocated MIMO Radar via Sparse Bayesian Learning

被引：1
|
作者：
Mao, Chenxing [1 ]
Wen, Fangqing [1 ]
机构：
[1] Yangtze Univ, Elect & Informat Sch, Jingzhou, Peoples R China
来源：
2019 INTERNATIONAL APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY SYMPOSIUM - CHINA (ACES), VOL 1 | 2019年
基金：
中国国家自然科学基金;
关键词：
array signal processing; MIMO radar; DOA estimation; sparse Bayesian learning; ARRIVAL ESTIMATION; ESPRIT;
D O I：
10.23919/aces48530.2019.9060628
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper deals with the problem of direction-of-arrival (DOA) estimation for colocated multiple-input multiple-output (MIMO) radar, and a computational efficient off-grid DOA estimator is proposed from the perspective of sparse Bayesian learning. The proposed estimator provides better DOA estimation accuracy than the existing peak searching algorithm. Numerical simulations have been carried out to show the effectiveness of the proposed algorithm.
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收藏
页数：2
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