中国物理B ›› 2015, Vol. 24 ›› Issue (11): 110201-110201.doi: 10.1088/1674-1056/24/11/110201

• GENERAL •    下一篇

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

文方青a b, 张弓a b, 贲德a b c   

  1. a College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    b Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing 210016, China;
    c Nanjing Research Institute of Electronics Technology, Nanjing 210039, China
  • 收稿日期:2015-03-27 修回日期:2015-06-18 出版日期:2015-11-05 发布日期:2015-11-05
  • 通讯作者: Zhang Gong E-mail:gzhang@nuaa.edu.cn
  • 基金资助:
    Project 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), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PADA), China.

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

Wen Fang-Qing (文方青)a b, Zhang Gong (张弓)a b, Ben De (贲德)a b c   

  1. a College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    b Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing 210016, China;
    c Nanjing Research Institute of Electronics Technology, Nanjing 210039, China
  • Received:2015-03-27 Revised:2015-06-18 Online:2015-11-05 Published:2015-11-05
  • Contact: Zhang Gong E-mail:gzhang@nuaa.edu.cn
  • Supported by:
    Project 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), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PADA), China.

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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 compressive 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 accurately 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, sparse Bayesian learning

Abstract: 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 compressive 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 accurately 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.

Key words: multiple-input multiple-output radar, random arrays, direction of arrival estimation, sparse Bayesian learning

中图分类号:  (Inverse problems)

  • 02.30.Zz
02.50.-r (Probability theory, stochastic processes, and statistics) 87.16.dt (Structure, static correlations, domains, and rafts)