Direction-of-arrival estimation for co-located multiple-input multiple-output radar using structural sparsity Bayesian learning

• 作者: 张弓 文方青 贲德
• 来源期刊: 中国物理B（英文版）
• 关键词: multiple-input multiple-output radar random arrays direction of arrival estimation sparse Bayesian learning
• 年，卷（期）: 2015,（11）
• 页码范围: 110201-1-110201-7
• 页数: 1页
• 语种: 英文
• DOI: 10.1088/1674-1056/24/11/110201

摘要

This paper addresses the direction of arrival (DOA) estimation problem for the co-located multiple-input multiple-output (MIMO) radar with random arrays. The spatially distributed sparsity of the targets in the background makes com-pressive sensing (CS) desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac-curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio (SNR) scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification (MUSIC) algorithm and other CS recovery algorithms.