Volume 13 Issue 1
Feb.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2024  >  13(1): 46-67
QUAN Yinghui, WU Yaojun, DUAN Lining, et al. A review of radar signal processing based on sparse recovery[J]. Journal of Radars, 2024, 13(1): 46–67. doi: 10.12000/JR23211
Citation: QUAN Yinghui, WU Yaojun, DUAN Lining, et al. A review of radar signal processing based on sparse recovery[J]. Journal of Radars, 2024, 13(1): 46–67. doi: 10.12000/JR23211
shu

A Review of Radar Signal Processing Based on Sparse Recovery

doi: 10.12000/JR23211
  • 1.

    School of Electronic Engineering, Xidian University, Xi’an 710071, China

  • 2.

    Xi’an Key Laboratory of Advanced Remote Sensing, Xi’an 710071, China

  • 3.

    State Key Laboratory of Millimeter Waves, School of Information Science and Engineering, Southeast University, Nanjing 210096, China

  • 4.

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an 710071, China

Funds:  The National Natural Science Foundation of China (62331019), The Shaanxi Provincial Science Fund for Distinguished Young Scholars (2021JC-23), The Science and Technology Innovation Team of Shaanxi Province (2019TD-002)
More Information
  • Corresponding author: QUAN Yinghui, yhquan@mail.xidian.edu.cn
  • Received Date: 2023-11-02
  • Rev Recd Date: 2024-01-06
    • Available Online: 2024-01-08
  • Publish Date: 2024-01-11
    Abstract
  • With the growing demand for radar target detection, Sparse Recovery (SR) technology based on the Compressive Sensing (CS) model has been widely used in radar signal processing. This paper first outlines the fundamental theory of SR and then introduces the sparse characteristics in radar signal processing from the perspectives of scene sparsity and observation sparsity. Subsequently, it explores these sparse properties to provide an overview of CS applications in radar signal processing, including spatial domain processing, pulse compression, coherent processing, radar imaging, and target detection. Finally, the paper summarizes the applications of CS in radar signal processing.

     

    • FullText(HTML)
  • loading
    • References(134)
  • [1]
    DE PRONY G R. Essai experimental et analytique: Sur les lois de la dilatabilite des fluides elastique et sur celles de la force expansive de la vapeur de l’eau et de la vapeur de l’alkool, a differentes temperatures[J]. Journal Polytechnique ou Bulletin du Travail fait a l’Ecole Centrale des Travaux Publics, 1795, 1(2): 24–76.
    [2]
    CANDÈS E J. Compressive sampling[C]. The International Congress of Mathematicians, Madrid, Spain, 2006: 1433–1452.
    [3]
    DONOHO D L and ELAD M. Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization[J]. Proceedings of the National Academy of Sciences of the United States of America, 2003, 100(5): 2197–2202. doi: 10.1073/pnas.0437847100
    [4]
    MALLAT S G and ZHANG Zhifeng. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397–3415. doi: 10.1109/78.258082
    [5]
    GREGOR K and LECUN Y. Learning fast approximations of sparse coding[C]. The 27th International Conference on International Conference on Machine Learning, Haifa, Israel, 2010: 399–406.
    [6]
    王谋, 韦顺军, 沈蓉, 等. 基于自学习稀疏先验的三维 SAR 成像方法[J]. 雷达学报, 2023, 12(1): 36–52. doi: 10.12000/JR22101

    WANG Mou, WEI Shunjun, SHEN Rong, et al. 3D SAR imaging method based on learned sparse prior[J]. Journal of Radars, 2023, 12(1): 36–52. doi: 10.12000/JR22101
    [7]
    胡磊, 周剑雄, 石志广, 等. 利用期望-最大化算法实现基于动态词典的压缩感知[J]. 电子与信息学报, 2012, 34(11): 2554–2560. doi: 10.3724/SP.J.1146.2012.00347

    HU Lei, ZHOU Jianxiong, SHI Zhiguang, et al. An EM-based approach for compressed sensing using dynamic dictionaries[J]. Journal of Electronics & Information Technology, 2012, 34(11): 2554–2560. doi: 10.3724/SP.J.1146.2012.00347
    [8]
    TANG Gongguo, BHASKAR B N, 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
    [9]
    CANDÈS E J, ROMBERG J, and TAO T. Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489–509. doi: 10.1109/TIT.2005.862083
    [10]
    CANDÈS E J and TAO T. Near-optimal signal recovery from random projections: Universal encoding strategies?[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406–5425. doi: 10.1109/TIT.2006.885507
    [11]
    DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582
    [12]
    SHOA A and SHIRANI S. Progressive coding of a gaussian source using matching pursuit[J]. IEEE Transactions on Signal Processing, 2008, 56(2): 636–649. doi: 10.1109/TSP.2007.907891
    [13]
    SAHOO S K and MAKUR A, et al. Signal recovery from random measurements via extended orthogonal matching pursuit[J]. IEEE Transactions on Signal Processing: A publication of the IEEE Signal Processing Society, 2015, 63(10): 2572–2581.
    [14]
    NEEDELL D and VERSHYNIN R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J]. Foundations of Computational Mathematics, 2009, 9(3): 317–334. doi: 10.1007/s10208-008-9031-3
    [15]
    HUANG Tianyao, LIU Yimin, XU Xingyu, et al. Analysis of frequency agile radar via compressed sensing[J]. IEEE Transactions on Signal Processing, 2018, 66(23): 6228–6240. doi: 10.1109/TSP.2018.2876301
    [16]
    WANG Lei, HUANG Tianyao, and LIU Yimin. Randomized stepped frequency radars exploiting block sparsity of extended targets: A theoretical analysis[J]. IEEE Transactions on Signal Processing, 2021, 69: 1378–1393. doi: 10.1109/TSP.2021.3058444
    [17]
    HUANG Tianyao, SHLEZINGER N, XU Xingyu, et al. Multi-carrier agile phased array radar[J]. IEEE Transactions on Signal Processing, 2020, 68: 5706–5721. doi: 10.1109/TSP.2020.3026186
    [18]
    BARANIUK R and STEEGHS P. Compressive radar imaging[C]. 2007 IEEE Radar Conference, Waltham, USA, 2007: 128–133.
    [19]
    GEDALYAHU K and ELDAR Y C. Time-delay estimation from low-rate samples: A union of subspaces approach[J]. IEEE Transactions on Signal Processing, 2010, 58(6): 3017–3031. doi: 10.1109/TSP.2010.2044253
    [20]
    KIM J M, LEE O K, and YE J C. Compressive MUSIC: Revisiting the link between compressive sensing and array signal processing[J]. IEEE Transactions on Information Theory, 2012, 58(1): 278–301. doi: 10.1109/TIT.2011.2171529
    [21]
    CHAN W L, CHARAN K, TAKHAR D, et al. A single-pixel terahertz imaging system based on compressed sensing[J]. Applied Physics Letters, 2008, 93(12): 121105. doi: 10.1063/1.2989126
    [22]
    FANNJIANG A C, STROHMER T, and YAN Pengchong. Compressed remote sensing of sparse objects[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 595–618. doi: 10.1137/090757034
    [23]
    MA Jianwei. Single-pixel remote sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 199–203. doi: 10.1109/LGRS.2008.2010959
    [24]
    BAJWA W U, LEUS G, SCAGLIONE A, et al. Special issue on compressive sensing in communications[J]. Physical Communication, 2012, 5(2): 61–63. doi: 10.1016/j.phycom.2011.11.003
    [25]
    HAUPT J, BAJWA W U, RAZ G, et al. Toeplitz compressed sensing matrices with applications to sparse channel estimation[J]. IEEE Transactions on Information Theory, 2010, 56(11): 5862–5875. doi: 10.1109/TIT.2010.2070191
    [26]
    LIANG Junhua, LIU Yang, ZHANG Wenjun, et al. Joint compressive sensing in wideband cognitive networks[C]. 2010 IEEE Wireless Communication and Networking Conference, Sydney, Australia, 2010: 1–5.
    [27]
    SEJDIĆ E, OROVIĆ I, and STANKOVIĆ S. Compressive sensing meets time-frequency: An overview of recent advances in time-frequency processing of sparse signals[J]. Digital Signal Processing, 2018, 77: 22–35. doi: 10.1016/j.dsp.2017.07.016
    [28]
    AXELSSON S R J. Analysis of random step frequency radar and comparison with experiments[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(4): 890–904. doi: 10.1109/TGRS.2006.888865
    [29]
    MARIC S V and TITLEBAUM E L. A class of frequency hop codes with nearly ideal characteristics for use in multiple-access spread-spectrum communications and radar and sonar systems[J]. IEEE Transactions on Communications, 1992, 40(9): 1442–1447. doi: 10.1109/26.163565
    [30]
    HERMAN M and STROHMER T. High-resolution radar via compressed sensing[J]. IEEE Transactions on Signal Processing, 2009, 57(6): 2275–2284. doi: 10.1109/TSP.2009.2014277
    [31]
    HERMAN M and STROHMER T. Compressed sensing radar[C]. 2008 IEEE Radar Conference, Rome, Italy, 2008: 1–6.
    [32]
    MISHALI M, ELDAR Y C, DOUNAEVSKY O, et al. Sub-nyquist acquisition hardware for wideband communication[C]. 2010 IEEE Workshop On Signal Processing Systems, San Francisco, USA, 2010: 156–161.
    [33]
    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
    [34]
    YU Yao, PETROPULU A P, and POOR H V. Compressive sensing for MIMO radar[C]. 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, China, 2009: 3017–3020.
    [35]
    文方青, 张弓, 陶宇, 等. 面向低信噪比的自适应压缩感知方法[J]. 物理学报, 2015, 64(8): 084301. doi: 10.7498/aps.64.084301

    WEN Fangqing, ZHANG Gong, TAO Yu, et al. Adaptive compressive sensing toward low signal-to-noise ratio scene[J]. Acta Physica Sinica, 2015, 64(8): 084301. doi: 10.7498/aps.64.084301
    [36]
    XIAO Peng, YU Ze, and LI Chunsheng. Compressive sensing SAR range compression with chirp scaling principle[J]. Science China Information Sciences, 2012, 55(10): 2292–2300. doi: 10.1007/s11432-012-4613-8
    [37]
    肖鹏, 李春升, 于泽. 合成孔径雷达压缩感知成像方法[J]. 北京航空航天大学学报, 2011, 37(11): 1333–1337.

    XIAO Peng, LI Chunsheng, and YU Ze. On compressive sensing applied to SAR imaging[J]. Journal of Beijing University of Aeronautics and Astronautics, 2011, 37(11): 1333–1337.
    [38]
    张玉玺, 孙进平, 张冰尘, 等. 基于压缩感知理论的多普勒解模糊处理[J]. 电子与信息学报, 2011, 33(9): 2103–2107. doi: 10.3724/SP.J.1146.2011.00073

    ZHANG Yuxi, SUN Jinping, ZHANG Bingchen, et al. Doppler ambiguity resolution based on compressive sensing theory[J]. Journal of Electronics & Information Technology, 2011, 33(9): 2103–2107. doi: 10.3724/SP.J.1146.2011.00073
    [39]
    王雪君, 孙进平, 张旭旺. 基于压缩感知的PD雷达序贯扩展卡尔曼滤波跟踪方法[J]. 信号处理, 2017, 33(4): 601–606. doi: 10.16798/j.issn.1003-0530.2017.04.022

    WANG Xuejun, SUN Jinping, and ZHANG Xuwang. New sequential extended kalman filter for pulse doppler radar tracker based on compressive sensing[J]. Journal of Signal Processing, 2017, 33(4): 601–606. doi: 10.16798/j.issn.1003-0530.2017.04.022
    [40]
    LEI Lei, LIE J P, GERSHMAN A B, et al. Robust adaptive beamforming in partly calibrated sparse sensor arrays[J]. IEEE Transactions on Signal Processing, 2010, 58(3): 1661–1667. doi: 10.1109/TSP.2009.2037852
    [41]
    WANG Jian, SHENG Weixing, HAN Yubing, et al. Adaptive beamforming with compressed sensing for sparse receiving array[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 823–833. doi: 10.1109/TAES.2014.120532
    [42]
    XIE Hu, FENG Dazheng, and YU Hongbo. Fast and robust adaptive beamforming method based on l1-norm constraint for large array[J]. Electronics Letters, 2015, 51(1): 98–99. doi: 10.1049/el.2014.2919
    [43]
    STOICA P, BABU P, and LI Jian. New method of sparse parameter estimation in separable models and its use for spectral analysis of irregularly sampled data[J]. IEEE Transactions on Signal Processing, 2011, 59(1): 35–47. doi: 10.1109/TSP.2010.2086452
    [44]
    BOURGUIGNON S, CARFANTAN H, and IDIER J. A sparsity-based method for the estimation of spectral lines from irregularly sampled data[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 575–585. doi: 10.1109/JSTSP.2007.910275
    [45]
    SHAH S, YU Yao, and PETROPULU A. Step-frequency radar with compressive sampling (SFR-CS)[C]. 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, USA, 2010: 1686–1689.
    [46]
    POTTER L C, ERTIN E, PARKER J T, et al. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE, 2010, 98(6): 1006–1020. doi: 10.1109/JPROC.2009.2037526
    [47]
    ZHANG Bingchen, HONG Wen, and WU Yirong. Sparse microwave imaging: Principles and applications[J]. Science China Information Sciences, 2012, 55(8): 1722–1754. doi: 10.1007/s11432-012-4633-4
    [48]
    JIANG Chenglong, ZHANG Bingchen, ZHANG Zhe, et al. Experimental results and analysis of sparse microwave imaging from spaceborne radar raw data[J]. Science China Information Sciences, 2012, 55(8): 1801–1815. doi: 10.1007/s11432-012-4634-3
    [49]
    CANDÈS E J and ROMBERG J K. Signal recovery from random projections[C]. SPIE 5674, Computational Imaging III, San Jose, United States, 2005: 76–86.
    [50]
    CANDÈS 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
    [51]
    TSAIG Y and DONOHO D L. Extensions of compressed sensing[J]. Signal Processing, 2006, 86(3): 549–571. doi: 10.1016/j.sigpro.2005.05.029
    [52]
    DONOHO D L and HUO Xiaoming. Uncertainty principles and ideal atomic decomposition[J]. IEEE Transactions on Information Theory, 2001, 47(7): 2845–2862. doi: 10.1109/18.959265
    [53]
    TANG Gongguo and NEHORAI A. Performance analysis of sparse recovery based on constrained minimal singular values[J]. IEEE Transactions on Signal Processing, 2011, 59(12): 5734–5745. doi: 10.1109/TSP.2011.2164913
    [54]
    CANDÈS E J and PLAN Y. A probabilistic and RIPless theory of compressed sensing[J]. IEEE Transactions on Information Theory, 2011, 57(11): 7235–7254. doi: 10.1109/TIT.2011.2161794
    [55]
    HÜGEL M, RAUHUT H, and STROHMER T. Remote sensing via ℓ1-minimization[J]. Foundations of Computational Mathematics, 2014, 14(1): 115–150. doi: 10.1007/s10208-013-9157-9
    [56]
    CANDÈS E J and TAO T. Decoding by linear programming[J]. IEEE Transactions on Information Theory, 2005, 51(12): 4203–4215. doi: 10.1109/TIT.2005.858979
    [57]
    黄天耀, 李宇涵, 王磊, 等. 相参频率捷变雷达目标稀疏重建性能边界综述[J]. 系统工程与电子技术, 2021, 43(7): 1729–1736. doi: 10.12305/j.issn.1001-506X.2021.07.01

    HUANG Tianyao, LI Yuhan, WANG Lei, et al. Review of performance bounds on sparse target recovery using coherent frequency agile radar[J]. Systems Engineering and Electronics, 2021, 43(7): 1729–1736. doi: 10.12305/j.issn.1001-506X.2021.07.01
    [58]
    CUI Hongyu and DUAN Huiping. Sparse Bayesian learning using correlated hyperparameters for recovery of block sparse signals[J]. Digital Signal Processing, 2017, 68: 24–30. doi: 10.1016/j.dsp.2017.05.003
    [59]
    ELDAR Y C and MISHALI M. Robust recovery of signals from a structured union of subspaces[J]. IEEE Transactions on Information Theory, 2009, 55(11): 5302–5316. doi: 10.1109/TIT.2009.2030471
    [60]
    BARANIUK R G, CEVHER V, DUARTE M F, et al. Model-based compressive sensing[J]. IEEE Transactions on Information Theory, 2010, 56(4): 1982–2001. doi: 10.1109/TIT.2010.2040894
    [61]
    张迪. 块稀疏信号的相位恢复方法研究[D]. [博士论文], 电子科技大学, 2023.

    ZHANG Di. Research on phase retrieval methods for block-sparse signals[D]. [Ph.D. dissertation], University of Electronic Science and Technology of China, 2023.
    [62]
    CANDÈS E and TAO T. The Dantzig selector: Statistical estimation when p is much larger than n[J]. The Annals of Statistics, 2007, 35(6): 2313–2351. doi: 10.1214/009053606000001523
    [63]
    BECK A and TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183–202. doi: 10.1137/080716542
    [64]
    NEEDELL D and VERSHYNIN R. Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 310–316. doi: 10.1109/JSTSP.2010.2042412
    [65]
    NEEDELL D and TROPP J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples[J]. Applied and Computational Harmonic Analysis, 2009, 26(3): 301–321. doi: 10.1016/j.acha.2008.07.002
    [66]
    TIPPING M E. Sparse Bayesian learning and the relevance vector machine[J]. The Journal of Machine Learning Research, 2001, 1: 211–244. doi: 10.1162/15324430152748236
    [67]
    XU Jiangpin, PI Yining, and CAO Zjie. Bayesian compressive sensing in synthetic aperture radar imaging[J]. IET Radar, Sonar & Navigation, 2012, 6(1): 2–8. doi: 10.1049/iet-rsn.2010.0375
    [68]
    杜文静. 基于稀疏贝叶斯学习的探地雷达成像算法研究[D]. [博士论文], 桂林电子科技大学, 2023.

    DU Wenjing. Research on imaging technology of ground penetrating radar based on sparse Bayesian learning[D]. [Ph.D. dissertation], Guilin University of Electronic Technology, 2023.
    [69]
    祁锐. 基于压缩感知的块稀疏信号重构算法及其应用研究[D]. [博士论文], 中国地质大学, 2018.

    QI Rui. Recovery algorithms of block sparse signal based on compressed sensing and it’s applications[D]. [Ph.D. dissertation], China University of Geosciences, 2018.
    [70]
    姚成勇. 块稀疏信号的结构化压缩感知重构算法研究[D]. [硕士论文], 重庆邮电大学, 2016.

    YAO Chengyong. Research of block-sparse signals reconstruction algorithms based on structured compressive sensing[D]. [Master dissertation], Chongqing University of Posts and Telecommunications, 2016.
    [71]
    KNILL C, SCHWEIZER B, SPARRER S, et al. High range and Doppler resolution by application of compressed sensing using low baseband bandwidth OFDM radar[J]. IEEE Transactions on Microwave Theory and Techniques, 2018, 66(7): 3535–3546. doi: 10.1109/TMTT.2018.2830389
    [72]
    黄天耀. 基于稀疏反演的相参捷变频雷达信号处理[D]. [博士论文], 清华大学, 2014.

    HUANG Tianyao. Coherent frequency-agile radar signal processing by solving an inverse problem with a sparsity constraint[D]. [Ph.D. dissertation], Tsinghua University, 2014.
    [73]
    全英汇, 方文, 沙明辉, 等. 频率捷变雷达波形对抗技术现状与展望[J]. 系统工程与电子技术, 2021, 43(11): 3126–3136. doi: 10.12305/j.issn.1001-506X.2021.11.11

    QUAN Yinghui, FANG Wen, SHA Minghui, et al. Present situation and prospects of frequency agility radar waveform countermeasures[J]. Systems Engineering and Electronics, 2021, 43(11): 3126–3136. doi: 10.12305/j.issn.1001-506X.2021.11.11
    [74]
    董淑仙, 全英汇, 沙明辉, 等. 捷变频雷达联合脉内频率编码抗间歇采样干扰[J]. 系统工程与电子技术, 2022, 44(11): 3371–3379. doi: 10.12305/j.issn.1001-506X.2022.11.11

    DONG Shuxian, QUAN Yinghui, SHA Minghui, et al. Frequency agile radar combined with intra-pulse frequency coding to resist intermittent sampling jamming[J]. Systems Engineering and Electronics, 2022, 44(11): 3371–3379. doi: 10.12305/j.issn.1001-506X.2022.11.11
    [75]
    Jankiraman M, Wessels B J, and Van Genderen P. Design of a Multifrequency FMCW Radar[C]. 1998 28th European Microwave Conference, Amsterdam, Netherlands, 1998: 584–589.
    [76]
    LELLOUCH G, TRAN P, PRIBIC R, et al. OFDM waveforms for frequency agility and opportunities for Doppler processing in radar[C]. 2008 IEEE Radar Conference, Rome, Italy, 2008: 1–6.
    [77]
    KNILL C, SCHWEIZER B, STEPHANY S, et al. FMCW-interference of frequency agile OFDM radars[C]. 2020 17th European Radar Conference, Utrecht, Netherlands, 2021: 160–163.
    [78]
    LIU Zhixing, QUAN Yinghui, WU Yaojun, et al. Range and Doppler reconstruction for sparse frequency agile linear frequency modulation-orthogonal frequency division multiplexing radar[J]. IET Radar, Sonar & Navigation, 2022, 16(6): 1014–1025. doi: 10.1049/rsn2.12239
    [79]
    SKOLNIK M, NEMHAUSER G, and SHERMAN J. Dynamic programming applied to unequally spaced arrays[J]. IEEE Transactions on Antennas and Propagation, 1964, 12(1): 35–43. doi: 10.1109/TAP.1964.1138163
    [80]
    KING D, PACKARD R, and THOMAS R. Unequally-spaced, broad-band antenna arrays[J]. IRE Transactions on Antennas and Propagation, 1960, 8(4): 380–384. doi: 10.1109/TAP.1960.1144876
    [81]
    SWENSON G and LO Y. The university of illinois radio telescope[J]. IRE Transactions on Antennas and Propagation, 1961, 9(1): 9–16. doi: 10.1109/TAP.1961.1144945
    [82]
    KUMAR B P and BRANNER G R. Generalized analytical technique for the synthesis of unequally spaced arrays with linear, planar, cylindrical or spherical geometry[J]. IEEE Transactions on Antennas and Propagation, 2005, 53(2): 621–634. doi: 10.1109/TAP.2004.841324
    [83]
    陈客松. 稀布天线阵列的优化布阵技术研究[D]. [博士论文], 电子科技大学, 2006.

    CHEN Kesong. Research on synthesis and optimization techniques of sparse antenna arrays[D]. [Ph.D. dissertation], University of Electronic Science and Technology of China, 2006.
    [84]
    胡斌. 基于压缩感知的稀疏阵列DOA估计关键技术研究[D]. [博士论文], 哈尔滨工业大学, 2020.

    HU Bin. Research on key technologies of DOA estimation of sparse array based on compressed sensing[D]. [Ph.D. dissertation], Harbin Institute of Technology, 2020.
    [85]
    季柄任. 稀疏孔径ISAR/InISAR成像算法研究[D]. [博士论文], 哈尔滨工业大学, 2021.

    JI Bingren. Research on methods of sparse apertuer ISAR/InISAR imaging[D]. [Ph.D. dissertation], Harbin Institute of Technology, 2021.
    [86]
    程增飞. 基于压缩感知的阵列信号处理技术研究[D]. [博士论文], 西安电子科技大学, 2017.

    CHENG Zengfei. Study on array signal processing technique based on compressed sensing[D]. [Ph.D. dissertation], Xidian University, 2017.
    [87]
    黄传禄, 晁坤, 毛云志. 基于压缩感知的空间谱估计[J]. 电波科学学报, 2014, 29(1): 150–157. doi: 10.13443/j.cjors.2013031101

    HUANG Chuanlu, CHAO Kun, and MAO Yunzhi. The spatial spectrum estimation based on compressive sensing[J]. Chinese Journal of Radio Science, 2014, 29(1): 150–157. doi: 10.13443/j.cjors.2013031101
    [88]
    郭月强, 陈建春, 王永军. 基于压缩感知的空域信号DOA估计[J]. 电子科技, 2013, 26(11): 39–41. doi: 10.3969/j.issn.1007-7820.2013.11.011

    GUO Yueqiang, CHEN Jianchun, and WANG Yongjun. Compressive sensing based narrowband signals DOA estimation[J]. Electronic Science and Technology, 2013, 26(11): 39–41. doi: 10.3969/j.issn.1007-7820.2013.11.011
    [89]
    SU Xiaolong, LIU Zhen, SHI Junpeng, et al. Real-valued deep unfolded networks for off-grid DOA estimation via nested array[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(4): 4049–4062. doi: 10.1109/TAES.2023.3235746
    [90]
    YANG Xiaopeng, SUN Yuze, ZENG Tao, et al. Fast STAP method based on PAST with sparse constraint for airborne phased array radar[J]. IEEE Transactions on Signal Processing, 2016, 64(17): 4550–4561. doi: 10.1109/TSP.2016.2569471
    [91]
    王安安, 谢文冲, 王永良. 基于稀疏恢复的双基地机载雷达杂波抑制方法[J/OL]. 系统工程与电子技术. http://kns.cnki.net/kcms/detail/11.2422.TN.20230313.1010.004.html, 2023.

    WANG An’an, XIE Wenchong, and WANG Yongliang. Bistatic airborne radar clutter suppression method based on sparse recovery[J/OL]. Systems Engineering and Electronics. http://kns.cnki.net/kcms/detail/11.2422.TN.20230313.1010.004.html, 2023.
    [92]
    CUI Ning, XING Kun, YU Zhongjun, et al. Tensor-based sparse recovery space-time adaptive processing for large size data clutter suppression in airborne radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(2): 907–922. doi: 10.1109/TAES.2022.3192223
    [93]
    王停, 张永斌, 王凡, 等. 压缩感知理论在稀疏阵列方向图综合中的应用研究[J]. 河北工业大学学报, 2020, 49(6): 22–27. doi: 10.14081/j.cnki.hgdxb.2020.06.004

    WANG Ting, ZHANG Yongbin, WANG Fan, et al. Study on application of compressed sensing theory in pattern synthesis for sparse array[J]. Journal of Hebei University of Technology, 2020, 49(6): 22–27. doi: 10.14081/j.cnki.hgdxb.2020.06.004
    [94]
    JIANG Siyi, FU Ning, WEI Zhiliang, et al. Compressed sampling for spectrum measurement and DOA estimation with array cooperative MWC[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 6504014. doi: 10.1109/TIM.2023.3291780
    [95]
    张小卫. 基于稀疏重构的DOA估计方法研究[D]. [博士论文], 哈尔滨工程大学, 2020.

    ZHANG Xiaowei. Research on direction-of-arrival estimation based on sparse reconstruction[D]. [Ph.D. dissertation], Harbin Engineering University, 2020.
    [96]
    YANG Zai, XIE Lihua, and ZHANG Cishen. A discretization-free sparse and parametric approach for linear array signal processing[J]. IEEE Transactions on Signal Processing, 2014, 62(19): 4959–4973. doi: 10.1109/TSP.2014.2339792
    [97]
    YU Gong, XIAO Shaoqiu, YU Zheng, et al. Synthesis of multiple-pattern planar arrays by the multitask Bayesian compressive sensing[J]. IEEE Antennas and Wireless Propagation Letters, 2021, 20(8): 1587–1591. doi: 10.1109/LAWP.2021.3091613
    [98]
    李少东, 裴文炯, 杨军. 基于CS的LFM信号脉冲压缩实现算法研究[J]. 雷达科学与技术, 2013, 11(3): 295–301. doi: 10.3969/j.issn.1672-2337.2013.03.014

    LI Shaodong, PEI Wenjiong, and YANG Jun. Pulse compression implementation of LFM signal via CS[J]. Radar Science and Technology, 2013, 11(3): 295–301. doi: 10.3969/j.issn.1672-2337.2013.03.014
    [99]
    庄钊文, 袁乃昌, 莫锦军, 等. 军用目标雷达散射截面预估与测量[M]. 北京: 科学出版社, 2007.

    ZHUANG Zhaowen, YUAN Naichang, MO Jinjun, et al. Estimation and Measurement of Radar Cross Section Area of Military Targets[M]. Beijing: Science Press, 2007.
    [100]
    孙艳艳. 压缩感知在雷达信号处理中的应用研究[D]. [硕士论文], 南京大学, 2013.

    SUN Yanyan. Study on compressive sensing in radar signal processing[D]. [Master dissertation], Nanjing University, 2013.
    [101]
    原慧, 盖玉刚, 刘淑普, 等. 基于压缩感知信号重构的宽带LFM雷达抗间歇采样转发干扰方法[J]. 舰船电子对抗, 2021, 44(6): 66–72, 103. doi: 10.16426/j.cnki.jcdzdk.2021.06.013

    YUAN Hui, GAI Yugang, LIU Shupu, et al. A method of wide-band LFM radar against interrupted-sampling repeater jamming based on compressed sensing signal reconstruction[J]. Shipboard Electronic Countermeasure, 2021, 44(6): 66–72, 103. doi: 10.16426/j.cnki.jcdzdk.2021.06.013
    [102]
    吴耀君. 脉间频率捷变雷达抗干扰研究[D]. [硕士论文], 西安电子科技大学, 2018.

    WU Yaojun. Research on anti-jamming performance of frequency agility radar[D]. [Master dissertation], Xidian University, 2018.
    [103]
    QUAN Yinghui, ZHANG Lei, XING Mengdao, et al. Velocity ambiguity resolving for moving target indication by compressed sensing[J]. Electronics Letters, 2011, 47(22): 1249–1251. doi: 10.1049/el.2011.1293
    [104]
    隋金坪, 刘振, 魏玺章, 等. 基于随机PRI压缩感知雷达的速度假目标识别方法[J]. 电子学报, 2017, 45(1): 98–103. doi: 10.3969/j.issn.0372-2112.2017.01.014

    SUI Jinping, LIU Zhen, WEI Xizhang, et al. Velocity false target identification based on random pulse repetition interval compressed sensing radar[J]. Acta Electronica Sinica, 2017, 45(1): 98–103. doi: 10.3969/j.issn.0372-2112.2017.01.014
    [105]
    LI Yuhan, HUANG Tianyao, XU Xingyu, et al. Phase transitions in frequency agile radar using compressed sensing[J]. IEEE Transactions on Signal Processing, 2021, 69: 4801–4818. doi: 10.1109/TSP.2021.3099629
    [106]
    QUAN Yinghui, WU Yaojun, LI Yachao, et al. Range-Doppler reconstruction for frequency agile and PRF-jittering radar[J]. IET Radar, Sonar & Navigation, 2018, 12(3): 348–352. doi: 10.1049/iet-rsn.2017.0421
    [107]
    KELLER J B. Geometrical theory of diffraction[J]. Journal of the Optical Society of America, 1962, 52(2): 116–130. doi: 10.1364/JOSA.52.000116
    [108]
    JAKOWATZ C V, WAHL D E, EICHEL P H, et al. Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach[M]. New York: Springer, 1996.
    [109]
    保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005.

    BAO Zheng, XING Mengdao, and WANG Tong. Radar Imaging Technique[M]. Beijing: Publishing House of Electronics Industry, 2005.
    [110]
    XU Gang, ZHANG Bangjie, YU Hanwen, et al. Sparse synthetic aperture radar imaging from compressed sensing and machine learning: Theories, applications, and trends[J]. IEEE Geoscience and Remote Sensing Magazine, 2022, 10(4): 32–69. doi: 10.1109/MGRS.2022.3218801
    [111]
    孙超. 基于压缩感知的高分辨雷达成像方法研究[D]. [博士论文], 西北工业大学, 2017.

    SUN Chao. Research on high-resolution radar imaging method based on compressed sensing[D]. [Ph.D. dissertation], Northwestern Polytechnical University, 2017.
    [112]
    王伟伟, 廖桂生, 吴孙勇, 等. 基于小波稀疏表示的压缩感知SAR成像算法研究[J]. 电子与信息学报, 2011, 33(6): 1440–1446. doi: 10.3724/SP.J.1146.2010.01171

    WANG Weiwei, LIAO Guisheng, WU Sunyong, et al. A compressive sensing imaging approach based on wavelet sparse representation[J]. Journal of Electronics & Information Technology, 2011, 33(6): 1440–1446. doi: 10.3724/SP.J.1146.2010.01171
    [113]
    ÇETIN M and KARL W C. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization[J]. IEEE Transactions on Image Processing, 2001, 10(4): 623–631. doi: 10.1109/83.913596
    [114]
    ALVER M B, SALEEM A, and ÇETIN M. Plug-and-play synthetic aperture radar image formation using deep priors[J]. IEEE Transactions on Computational Imaging, 2021, 7: 43–57. doi: 10.1109/TCI.2020.3047473
    [115]
    杨悦. 合成孔径雷达结构化目标稀疏成像方法研究[D]. [博士论文], 电子科技大学, 2020.

    YANG Yue. Synthetic aperture radar sparse imaging method for structured target[D]. [Ph.D. Dissertation], University of Electronic Science and Technology of China, 2020.
    [116]
    XU Gang, XIA Xianggen, and HONG Wei. Nonambiguous SAR image formation of maritime targets using weighted sparse approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(3): 1454–1465. doi: 10.1109/TGRS.2017.2763147
    [117]
    COKER J D and TEWFIK A H. Compressed sensing and multistatic SAR[C]. 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, Taipei, China, 2009: 1097–1100.
    [118]
    STOJANOVIC I and KARL W C. Imaging of moving targets with multi-static SAR using an overcomplete dictionary[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(1): 164–176. doi: 10.1109/JSTSP.2009.2038982
    [119]
    李晶, 张顺生, 常俊飞. 基于压缩感知的双基SAR二维高分辨成像算法[J]. 信号处理, 2012, 28(5): 737–743. doi: 10.3969/j.issn.1003-0530.2012.05.019

    LI Jing, ZHANG Shunsheng, and CHANG Junfei. Two-dimensional high resolution bistatic SAR imaging algorithm based on compressed sensing[J]. Signal Processing, 2012, 28(5): 737–743. doi: 10.3969/j.issn.1003-0530.2012.05.019
    [120]
    ZHANG Lei, QIAO Zhijun, XING Mengdao, et al. High-resolution ISAR imaging by exploiting sparse apertures[J]. IEEE Transactions on Antennas and Propagation, 2012, 60(2): 997–1008. doi: 10.1109/TAP.2011.2173130
    [121]
    XU Gang, XING Mengdao, XIA Xianggen, et al. High-resolution inverse synthetic aperture radar imaging and scaling with sparse aperture[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(8): 4010–4027. doi: 10.1109/JSTARS.2015.2439266
    [122]
    ZHANG Lei, XING Mengdao, QIU Chengwei, et al. Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(10): 3824–3838. doi: 10.1109/TGRS.2010.2048575
    [123]
    ZHAO Guanghui, WANG Zhengyang, WANG Qi, et al. Robust ISAR imaging based on compressive sensing from noisy measurements[J]. Signal Processing, 2012, 92(1): 120–129. doi: 10.1016/j.sigpro.2011.06.011
    [124]
    ZHANG Xiaohua, BAI Ting, MENG Hongyun, et al. Compressive sensing-based ISAR imaging via the combination of the sparsity and nonlocal total variation[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(5): 990–994. doi: 10.1109/LGRS.2013.2284288
    [125]
    HU Changyu, LI Ze, WANG Ling, et al. Inverse synthetic aperture radar imaging using a deep ADMM network[C]. 2019 20th International Radar Symposium, Ulm, Germany, 2019: 1–9.
    [126]
    XU Gang, XING Mengdao, ZHANG Lei, et al. Bayesian inverse synthetic aperture radar imaging[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(6): 1150–1154. doi: 10.1109/LGRS.2011.2158797
    [127]
    LIU Jihong, LI Xiang, XU Shaokun, et al. ISAR imaging of non-uniform rotation targets with limited pulses via compressed sensing[J]. Progress in Electromagnetics Research B, 2012, 41: 285–305. doi: 10.2528/PIERB12041715
    [128]
    SUN Chao, WANG Baoping, FANG Yang, et al. High-resolution ISAR imaging of maneuvering targets based on sparse reconstruction[J]. Signal Processing, 2015, 108: 535–548. doi: 10.1016/j.sigpro.2014.10.027
    [129]
    RAGHAVAN R S. Analysis of CA-CFAR processors for linear-law detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(3): 661–665. doi: 10.1109/7.256288
    [130]
    李莹, 张弓, 陶宇, 等. 基于压缩感知的步进频雷达目标检测算法[J]. 现代雷达, 2015, 37(9): 22–25. doi: 10.16592/j.cnki.1004-7859.2015.09.005

    LI Ying, ZHANG Gong, TAO Yu, et al. Target detection in compressive sensing based on step frequency radar[J]. Modern Radar, 2015, 37(9): 22–25. doi: 10.16592/j.cnki.1004-7859.2015.09.005
    [131]
    刘长远, 马俊虎, 甘露. 基于压缩感知的CFAR目标检测在机会雷达中的应用[J]. 太赫兹科学与电子信息学报, 2018, 16(4): 630–636. doi: 10.11805/TKYDA201804.0630

    LIU Changyuan, MA Junhu, and GAN Lu. Application of CA-CFAR with Compressive Sensing in opportunistic radar[J]. Journal of Terahertz Science and Electronic Information Technology, 2018, 16(4): 630–636. doi: 10.11805/TKYDA201804.0630
    [132]
    张杏杏. 基于压缩感知的雷达恒虚警率检测算法的研究[D]. [硕士论文], 大连海事大学, 2019.

    ZHANG Xingxing. Rearch on radar constant false alarm rate detection algorithm based on compressed sensing[D]. [Master dissertation], Dalian Maritime University, 2019.
    [133]
    NA Siqi, HUANG Tianyao, LIU Yimin, et al. Compressed sensing radar detectors under the row-orthogonal design model: A statistical mechanics perspective[J]. IEEE Transactions on Signal Processing, 2023, 71: 2668–2682. doi: 10.1109/TSP.2023.3297743
    [134]
    ZHANG Xiaowei, LI Ming, ZUO Lei, et al. Compressed sensing detector for wideband radar using the dominant scatterer[J]. IEEE Signal Processing Letters, 2014, 21(10): 1275–1279. doi: 10.1109/LSP.2014.2332640
    • 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(1001) PDF downloads(274) 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