Volume 10 Issue 6
Dec.  2021
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2021  >  10(6): 919-928
CUI Ning, XING Kun, DUAN Keqing, et al. Fast tensor-based three-dimensional sparse Bayesian learning space-time adaptive processing method[J]. Journal of Radars, 2021, 10(6): 919–928. doi: 10.12000/JR21140
Citation: CUI Ning, XING Kun, DUAN Keqing, et al. Fast tensor-based three-dimensional sparse Bayesian learning space-time adaptive processing method[J]. Journal of Radars, 2021, 10(6): 919–928. doi: 10.12000/JR21140
shu

Fast Tensor-based Three-dimensional Sparse Bayesian Learning Space-Time Adaptive Processing Method

doi: 10.12000/JR21140
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • 2.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China

Funds:  The National Natural Science Foundation of China (61871397)
More Information
  • Corresponding author: DUAN Keqing, duankeqing@aliyun.com; YU Zhongjun, yuzj@ucas.ac.cn
  • Received Date: 2021-09-26
  • Accepted Date: 2021-12-06
  • Rev Recd Date: 2021-12-03
    • Available Online: 2021-12-07
  • Publish Date: 2021-12-23
    Abstract
  • When airborne radar is applied to the non-side-looking mode, moving target detection performance considerably degrades because of the nonstationary clutter. Conventional three-dimensional (3D) Space-Time Adaptive Processing (STAP) can effectively eliminate the nonstationary clutter via adaptively constructing an elevation-azimuth-Doppler 3D filter. However, large system degrees of freedom lead to a shortage of training samples in a heterogeneous environment. Although introducing the Sparse Recovery (SR) technology substantially reduces the sample requirement, the practical application of this technology is limited by computational complexities. To solve the above problems, this paper proposes a fast 3D sparse Bayesian learning STAP, based on the third-order tensor structure of echo data. In the proposed method, large-scale matrix calculation is decomposed into small-scale matrix calculation using a low-complexity tensor-based operation, thus considerably reducing the computational load. Exhaustive numerical experiments verify that the proposed method directly reduces the computational load by several orders of magnitude compared with that of the existing SR-STAP algorithms, while maintaining the SR-STAP performance. Therefore, the tensor-based method is a superior processing method than the vector-based method in engineering.

     

    • FullText(HTML)
  • loading
    • References(24)
  • [1]
    REED I S, MALLETT J D, and BRENNAN L E. Rapid convergence rate in adaptive arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 1974, AES-10(6): 853–863. doi: 10.1109/TAES.1974.307893
    [2]
    谢文冲, 段克清, 王永良. 机载雷达空时自适应处理技术研究综述[J]. 雷达学报, 2017, 6(6): 575–586. doi: 10.12000/JR17073

    XIE Wenchong, DUAN Keqing, and WANG Yongliang. Space time adaptive processing technique for airborne radar: An overview of its development and prospects[J]. Journal of Radars, 2017, 6(6): 575–586. doi: 10.12000/JR17073
    [3]
    MENG Xiangdong, WANG Tong, WU Jianxin, et al. Short-range clutter suppression for airborne radar by utilizing prefiltering in elevation[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 268–272. doi: 10.1109/LGRS.2008.2012126
    [4]
    DUAN Keqing, XU Hong, YUAN Huadong, et al. Reduced-DOF three-dimensional STAP via subarray synthesis for nonsidelooking planar array airborne radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(4): 3311–3325. doi: 10.1109/TAES.2019.2958174
    [5]
    SUN Ke, MENG Huadong, WANG Yongliang, et al. Direct data domain STAP using sparse representation of clutter spectrum[J]. Signal Processing, 2011, 91(9): 2222–2236. doi: 10.1016/j.sigpro.2011.04.006
    [6]
    YANG Zhaocheng, LI Xiang, WANG Hongqiang, et al. On clutter sparsity analysis in space-time adaptive processing airborne radar[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1214–1218. doi: 10.1109/LGRS.2012.2236639
    [7]
    DUAN Keqing, XU Hong, YUAN Huadong, et al. Three-dimensional sparse recovery space-time adaptive processing for airborne radar[J]. The Journal of Engineering, 2019, 2019(19): 5478–5482. doi: 10.1049/joe.2019.0343
    [8]
    GUO Yiduo, LIAO Guisheng, and FENG Weike. Sparse representation based algorithm for airborne radar in beam-space post-Doppler reduced-dimension space-time adaptive processing[J]. IEEE Access, 2017, 5: 5896–5903. doi: 10.1109/ACCESS.2017.2689325
    [9]
    HAN Sudan, FAN Chongyi, and HUANG Xiaotao. A novel STAP based on spectrum-aided reduced-dimension clutter sparse recovery[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(2): 213–217. doi: 10.1109/LGRS.2016.2635104
    [10]
    WANG Zetao, XIE Wenchong, DUAN Keqing, et al. Clutter suppression algorithm based on fast converging sparse Bayesian learning for airborne radar[J]. Signal Processing, 2017, 130: 159–168. doi: 10.1016/j.sigpro.2016.06.023
    [11]
    QIU Wei, ZHOU Jianxiong, ZHAO Hongzhong, et al. Three-dimensional sparse turntable microwave imaging based on compressive sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 826–830. doi: 10.1109/LGRS.2014.2363238
    [12]
    SIDIROPOULOS N D, DE LATHAUWER L, FU Xiao, et al. Tensor decomposition for signal processing and machine learning[J]. IEEE Transactions on Signal Processing, 2017, 65(13): 3551–3582. doi: 10.1109/TSP.2017.2690524
    [13]
    ZHAO Rongqiang, WANG Qiang, FU Jun, et al. Exploiting block-sparsity for hyperspectral Kronecker compressive sensing: A tensor-based Bayesian method[J]. IEEE Transactions on Image Processing, 2020, 29: 1654–1668. doi: 10.1109/TIP.2019.2944722
    [14]
    姜磊, 王彤. 机载雷达自适应对角加载参数估计方法[J]. 电子与信息学报, 2016, 38(7): 1752–1757. doi: 10.11999/JEIT151003

    JIANG Lei and WANG Tong. An adaptive estimation method of diagonal loading parameter for airborne radar[J]. Journal of Electronics &Information Technology, 2016, 38(7): 1752–1757. doi: 10.11999/JEIT151003
    [15]
    MA Zeqiang, LIU Yimin, MENG Huadong, et al. Jointly sparse recovery of multiple snapshots in STAP[C]. 2013 IEEE Radar Conference, Ottawa, Canada, 2013: 1–4. doi: 10.1109/RADAR.2013.6586083
    [16]
    DUAN Keqing, WANG Zetao, XIE Wenchong, et al. Sparsity-based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar[J]. IET Signal Processing, 2017, 11(5): 544–553. doi: 10.1049/iet-spr.2016.0183
    [17]
    TROPP J A and GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655–4666. doi: 10.1109/TIT.2007.909108
    [18]
    YANG Zhaocheng, LI Xiang, WANG Hongqiang, et al. Adaptive clutter suppression based on iterative adaptive approach for airborne radar[J]. Signal Processing, 2013, 93(12): 3567–3577. doi: 10.1016/j.sigpro.2013.03.033
    [19]
    YANG Shuyuan, LI Bin, WANG Min, et al. Compressive direction-of-arrival estimation via regularized multiple measurement FOCUSS algorithm[C]. 2014 International Joint Conference on Neural Networks, New York, USA, 2014: 2800–2803. doi: 10.1109/IJCNN.2014.6889967.
    [20]
    BADER B W and KOLDA T G. Matlab tensor toolbox version 2.6[EB/OL]. http://www.sandia.gov/tgkolda/TensorToolbox. 2015.
    [21]
    GUERCI J R. Space-Time Adaptive Processing for Radar[M]. Boston: Artech House, 2014: 52–73.
    [22]
    HALE T B, TEMPLE M A, RAQUET J F, et al. Localized three-dimensional adaptive spatial-temporal processing for airborne radar[C]. 2002 International Radar Conference, Edinburgh, UK, 2002: 191–195. doi: 10.1049/cp:20020275.
    [23]
    洪玺, 王文杰, 殷勤业. 基于多级维纳滤波器的空时自适应信号处理及其在无线通信系统中的应用[J]. 信号处理, 2017, 33(3): 430–436. doi: 10.16798/j.issn.1003-0530.2017.03.026

    HONG Xi, WANG Wenjie, and YIN Qinye. Multistage wiener filter based space and time adaptive signal processing and its application in wireless communication system[J]. Journal of Signal Processing, 2017, 33(3): 430–436. doi: 10.16798/j.issn.1003-0530.2017.03.026
    [24]
    王璐, 吴仁彪. 直接数据域空时自适应单脉冲方法[J]. 系统工程与电子技术, 2016, 38(12): 2738–2744. doi: 10.3969/j.issn.1001-506X.2016.12.09

    WANG Lu and WU Renbiao. Direct data domain space-time adaptive monopulse method[J]. Systems Engineering and Electronics, 2016, 38(12): 2738–2744. doi: 10.3969/j.issn.1001-506X.2016.12.09
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(1781) PDF downloads(139) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return