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

作　　者：文方青 张弓 贲德 

机构地区：[1]College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics [2]Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education [3]Nanjing Research Institute of Electronics Technology

出　　处：《Chinese Physics B》2015年第11期70-76,共7页中国物理B（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.61071163,61271327,and 61471191);the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics,China(Grant No.BCXJ14-08);the Funding of Innovation Program for Graduate Education of Jiangsu Province,China(Grant No.KYLX 0277);the Fundamental Research Funds for the Central Universities,China(Grant No.3082015NP2015504);the Priority Academic Program Development of Jiangsu Higher Education Institutions(PADA),China

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.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.

关 键 词：multiple-input multiple-output radar random arrays direction of arrival estimation sparseBayesian learning 

分 类 号：TN957.51[电子电信—信号与信息处理]