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Volume 43 Issue 5
May  2021
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Tao ZHANG, Juncheng GUO, Ran LAI. Gridless Sparse Recovery for Non-sidelooking Space-Time Adaptive Processing Based on Atomic Norm Minimization[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1235-1242. doi: 10.11999/JEIT200114
Citation: Tao ZHANG, Juncheng GUO, Ran LAI. Gridless Sparse Recovery for Non-sidelooking Space-Time Adaptive Processing Based on Atomic Norm Minimization[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1235-1242. doi: 10.11999/JEIT200114

Gridless Sparse Recovery for Non-sidelooking Space-Time Adaptive Processing Based on Atomic Norm Minimization

doi: 10.11999/JEIT200114
Funds:  The National Natural Science Foundation of China (U1733116), The Fundamental Research Foundation for Central Universities-CAUC(3122019048), The Young Scholar Foundation of Civil Aviation University of China
  • Received Date: 2020-02-21
  • Rev Recd Date: 2020-11-26
  • Available Online: 2020-12-01
  • Publish Date: 2021-05-18
  • Sparse recovery Space-Time Adaptive Processing (STAP) can reduce the requirements of clutter samples, and suppress effectively clutter using limited training samples for airborne radar. The whole space-time plane is discretized into small grid points uniformly in presently available sparse recovery STAP methods, however, the clutter ridge is not located exactly on the pre-discretized grid points in non-sidelooking STAP radar. The dictionary mismatch effect degrades the performance of STAP significantly. In this paper, a gridless sparse recovery STAP method is proposed based on Atomic Norm Minimization (ANM-STAP), which utilizes the low-rank property of the clutter covariance matrix. In the proposed method, the clutter spectrum is precisely estimated in continuous space-time plane without dictionary mismatch. Numerical results show that the proposed method provides an improved performance to the sparse recovery STAP methods with discretized dictionaries.
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  • [1]
    KLEMM R. Applications of Space-time Adaptive Processing[M]. London: Institution of Electrical Engineers, 2004.
    [2]
    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
    [3]
    DUAN Keqing, YUAN Huadong, XU Hong, et al. Sparsity-based non-stationary clutter suppression technique for airborne radar[J]. IEEE Access, 2018, 6: 56162–56169. doi: 10.1109/ACCESS.2018.2873021
    [4]
    CANDES E J and WAKIN M B. An introduction to compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 21–30. doi: 10.1109/MSP.2007.914731
    [5]
    ENDER J H G. On compressive sensing applied to radar[J]. Signal Processing, 2010, 90(5): 1402–1414. doi: 10.1016/j.sigpro.2009.11.009
    [6]
    阳召成, 黎湘, 王宏强. 基于空时功率谱稀疏性的空时自适应处理技术研究进展[J]. 电子学报, 2014, 42(6): 1194–1204. doi: 10.3969/j.issn.0372-2112.2014.06.024

    YANG Zhaocheng, LI Xiang, and WANG Hongqiang. An overview of space-time adaptive processing technology based on sparsity of space-time power spectrum[J]. Acta Electronica Sinica, 2014, 42(6): 1194–1204. doi: 10.3969/j.issn.0372-2112.2014.06.024
    [7]
    马泽强, 王希勤, 刘一民, 等. 基于稀疏恢复的空时二维自适应处理技术研究现状[J]. 雷达学报, 2014, 3(2): 217–228. doi: 10.3724/SP.J.1300.2014.14002

    MA Zeqiang, WANG Xiqin, LIU Yimin, et al. An overview on sparse recovery-based STAP[J]. Journal of Radars, 2014, 3(2): 217–228. doi: 10.3724/SP.J.1300.2014.14002
    [8]
    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
    [9]
    孙珂, 张颢, 李刚, 等. 基于杂波谱稀疏恢复的空时自适应处理[J]. 电子学报, 2011, 39(6): 1389–1393.

    SUN Ke, ZHANG Hao, LI Gang, et al. STAP via sparse recovery of clutter spectrum[J]. Acta Electronica Sinica, 2011, 39(6): 1389–1393.
    [10]
    YANG Zhaocheng, QIN Yuliang, DE LAMARE R C, et al. Sparsity-based direct data domain space-time adaptive processing with intrinsic clutter motion[J]. Circuits, Systems, and Signal Processing, 2017, 36(1): 219–246. doi: 10.1007/s00034-016-0301-z
    [11]
    WANG Lei, LIU Yimin, MA Zeqiang, et al. A novel STAP method based on structured sparse recovery of clutter spectrum[C]. 2015 IEEE Radar Conference, Arlington, USA, 2015: 561–565. doi: 10.1109/RADAR.2015.7131061.
    [12]
    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
    [13]
    SUN Yuze, YANG Xiaopeng, LONG Teng, et al. Robust sparse Bayesian learning STAP method for discrete interference suppression in nonhomogeneous clutter[C]. 2017 IEEE Radar Conference, Seattle, USA, 2017: 1003–1008. doi: 10.1109/RADAR.2017.7944350.
    [14]
    吕晓德, 杨璟茂, 岳琦, 等. 基于稀疏贝叶斯学习的机载双基雷达杂波抑制[J]. 电子与信息学报, 2018, 40(11): 2651–2658. doi: 10.11999/JEIT180062

    LV Xiaode, YANG Jingmao, YUE Qi, et al. Airborne bistatic radar clutter suppression based on sparse Bayesian learning[J]. Journal of Electronics &Information Technology, 2018, 40(11): 2651–2658. doi: 10.11999/JEIT180062
    [15]
    YANG Zhaocheng, LI Xiang, WANG Hongqiang, et al. Knowledge-aided STAP with sparse-recovery by exploiting spatio-temporal sparsity[J]. IET Signal Processing, 2016, 10(2): 150–161. doi: 10.1049/iet-spr.2014.0255
    [16]
    DUAN Keqing, LIU Weijian, DUAN Guangqing, et al. Off-grid effects mitigation exploiting knowledge of the clutter ridge for sparse recovery STAP[J]. IET Radar, Sonar & Navigation, 2018, 12(5): 557–564. doi: 10.1049/iet-rsn.2017.0425
    [17]
    BAI Gatai, TAO Ran, ZHAO Juan, et al. Parameter-searched OMP method for eliminating basis mismatch in space-time spectrum estimation[J]. Signal Processing, 2017, 138: 11–15. doi: 10.1016/j.sigpro.2017.03.003
    [18]
    CANDÈS E J and FERNANDEZ‐GRANDA C. Towards a mathematical theory of super-resolution[J]. Communications on Pure and Applied Mathematics, 2014, 67(6): 906–956. doi: 10.1002/cpa.21455
    [19]
    TANG Gongguo, BHASKAR B, SHAH P, et al. Compressed sensing off the grid[J]. IEEE Transactions on Information Theory, 2013, 59(11): 7465–7490. doi: 10.1109/TIT.2013.2277451
    [20]
    CHI Yuejie and CHEN Yuxin. Compressive two-dimensional harmonic retrieval via atomic norm minimization[J]. IEEE Transactions on Signal Processing, 2015, 63(4): 1030–1042. doi: 10.1109/TSP.2014.2386283
    [21]
    YANG Zai, XIE Lihua, and STOICA P. Vandermonde decomposition of multilevel Toeplitz matrices with application to multidimensional super-resolution[J]. IEEE Transactions on Information Theory, 2016, 62(6): 3685–3701. doi: 10.1109/TIT.2016.2553041
    [22]
    SEN S. Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(8): 1510–1523. doi: 10.1109/JSTSP.2015.2464187
    [23]
    GINI F and GRECO M. Covariance matrix estimation for CFAR detection in correlated heavy tailed clutter[J]. Signal Processing, 2002, 82(12): 1847–1859. doi: 10.1016/S0165-1684(02)00315-8
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