Image reconstruction and compressive sensing in MIMO radar

被引:4
|
作者
Sun, Bing [2 ]
Lopez, Juan
Qiao, Zhijun [1 ,3 ]
机构
[1] Univ Texas Pan Amer, Dept Math, 1201 West Univ Dr, Edinburg, TX 78539 USA
[2] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
[3] Univ Texas Pan Amer, Dept Math, Edinburg, TX 78539 USA
来源
RADAR SENSOR TECHNOLOGY XVIII | 2014年 / 9077卷
关键词
Multiple-input multiple-output; compressive sensing; basis pursuit; total-variation minimization;
D O I
10.1117/12.2051275
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
Multiple-input multiple-output (MIMO) radar utilizes the flexible configuration of transmitting and receiving antennas to construct images of target scenes. Because of the target scenes' sparsity, the compressive sensing (CS) technique can be used to realize a feasible reconstruction of the target scenes from undersampling data. This paper presents the signal model of MIMO radar and derive the corresponding CS measurement matrix, which shows success of the CS technique. Also the basis pursuit method and total-variation minimization method are adopted for different scenes' recovery. Numerical simulations are provided to illustrate the validity of reconstruction for one dimensional and two dimensional scenes.
引用
收藏
页数:7
相关论文
共 50 条
  • [1] COMPRESSIVE SENSING FOR MIMO RADAR
    Yu, Yao
    Petropulu, Athina P.
    Poor, H. Vincent
    2009 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1- 8, PROCEEDINGS, 2009, : 3017 - +
  • [2] Spatial Compressive Sensing for MIMO Radar
    Rossi, Marco
    Haimovich, Alexander M.
    Eldar, Yonina C.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (02) : 419 - 430
  • [3] Three Dimensional Compressive Sensing in MIMO Radar
    Liu, Yaqi
    Zhang, Yongquan
    Tang, Jun
    Zhang, Ning
    Zhu, Wei
    2015 49TH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS AND COMPUTERS, 2015, : 599 - 603
  • [4] Time-Slotted FMCW MIMO ISAR with Compressive Sensing Image Reconstruction
    Bacci, A.
    Giusti, E.
    Tomei, S.
    Martorella, M.
    Berizzi, F.
    2015 3RD INTERNATIONAL WORKSHOP ON COMPRESSED SENSING THEORY AND ITS APPLICATION TO RADAR, SONAR, AND REMOTE SENSING (COSERA), 2015, : 229 - 233
  • [5] Sparse Arrays, MIMO, and Compressive Sensing for GMTI Radar
    Kim, Haley H.
    Haimovich, Alexander M.
    Govoni, Mark A.
    CONFERENCE RECORD OF THE 2014 FORTY-EIGHTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, 2014, : 849 - 853
  • [6] Compressive Sensing for improved MIMO Radar performance - A Review
    Hadi, Muhammad Abdul
    Alshebeili, Saleh
    Abd El-Samie, Fathi E.
    Jamil, Khalid
    2015 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY RESEARCH (ICTRC), 2015, : 270 - 273
  • [7] High Resolution MIMO Radar Sensing With Compressive Illuminations
    Sugavanam, Nithin
    Baskar, Siddharth
    Ertin, Emre
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2022, 70 : 1448 - 1463
  • [8] CSSF MIMO RADAR: Compressive-Sensing and Step-Frequency Based MIMO Radar
    Yu, Yao
    Petropulu, Athina P.
    Poor, H. Vincent
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2012, 48 (02) : 1490 - 1504
  • [9] Exploiting Compressive Sensing Basis Selection to Improve 2 x 2 MIMO Radar Image
    Rojhani, Neda
    Passafiume, Marco
    Lucarelli, Matteo
    Collodi, Giovanni
    Cidronali, Alessandro
    2020 IEEE MTT-S INTERNATIONAL CONFERENCE ON MICROWAVES FOR INTELLIGENT MOBILITY (ICMIM), 2020,
  • [10] CONDITIONS FOR TARGET RECOVERY IN SPATIAL COMPRESSIVE SENSING FOR MIMO RADAR
    Rossi, M.
    Haimovich, A. M.
    Eldar, Y. C.
    2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2013, : 4115 - 4119