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LIAO Zhipeng, DUAN Keqing, and GAO Fei. Collaborative nonlinear space-time adaptive processing and pulse compression based on neural networks[J]. Journal of Radars, in press. doi: 10.12000/JR25096
Citation: LIAO Zhipeng, DUAN Keqing, and GAO Fei. Collaborative nonlinear space-time adaptive processing and pulse compression based on neural networks[J]. Journal of Radars, in press. doi: 10.12000/JR25096
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Collaborative Nonlinear Space-time Adaptive Processing and Pulse Compression Based on Neural Networks

DOI: 10.12000/JR25096 CSTR: 32380.14.JR25096
  • 1.

    School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

  • 2.

    China Academy Space Technology Xian Branch, Xi’an 710100, China

Funds:  The Foundation of National Key Laboratory of Radar Signal Processing (JKW202302)
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  • Corresponding author: DUAN Keqing, duankeqing@aliyun.com
  • Received Date: 2025-05-23
  • Rev Recd Date: 2025-07-28
    • Available Online: 2025-08-04
    Abstract
  • Traditional airborne radar Pulse Compression (PC) and Space-Time Adaptive Processing (STAP) suffer performance degradation in complex target and clutter environments due to their reliance on predefined linear models. To address this issue, we developed a deep learning-based joint STAP-PC technique. This approach employed dedicated networks—a super-resolution space-time spectrum network for nonlinear clutter estimation and a PC network for nonlinear PC. The proposed architecture effectively mitigated model mismatch within the processing chain, leading to improved clutter suppression and target detection. Notably, we mathematically established the feasibility of post-pulse compensation to prevent nonlinear PC from introducing phase errors across elements and pulses. The implemented architecture utilized multimodule convolutional neural networks for super-resolution space-time spectrum estimation and PC, with each module’s functionality demonstrating clear mathematical correspondence, thereby ensuring the reliability of the overall processing chain. Simulation results revealed that in scenarios with dense weak targets and limited samples, the proposed nonlinear joint processing technique improved signal-to-clutter-plus-noise ratio by approximately 20 dB over traditional methods.

     

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    • References(44)
  • [1]
    SKOLNIK M I. Radar Handbook[M]. 3rd ed. New York: McGraw-Hill, 2008.
    [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]
    RICHARDS M A. Fundamentals of Radar Signal Processing[M]. New York: McGraw-Hill, 2005.
    [4]
    ACKROYD M H and GHANI F. Optimum mismatched filters for sidelobe suppression[J]. IEEE Transactions on Aerospace and Electronic Systems, 1973, AES-9(2): 214–218. doi: 10.1109/TAES.1973.309769.
    [5]
    TSAO J and STEINBERG B D. Reduction of sidelobe and speckle artifacts in microwave imaging: The CLEAN technique[J]. IEEE Transactions on Antennas and Propagation, 1988, 36(4): 543–556. doi: 10.1109/8.1144.
    [6]
    RABASTE O and SAVY L. Mismatched filter optimization for radar applications using quadratically constrained quadratic programs[J]. IEEE Transactions on Aerospace and Electronic Systems, 2015, 51(4): 3107–3122. doi: 10.1109/TAES.2015.130769.
    [7]
    RABASTE O and BOSSE J. Robust mismatched filter for off-grid target[J]. IEEE Signal Processing Letters, 2019, 26(8): 1147–1151. doi: 10.1109/LSP.2019.2923054.
    [8]
    BLUNT S D and GERLACH K. Adaptive pulse compression via MMSE estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(2): 572–584. doi: 10.1109/TAES.2006.1642573.
    [9]
    GERLACH K and BLUNT S D. Radar pulse compression repair[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(3): 1188–1195. doi: 10.1109/TAES.2007.4383610.
    [10]
    BLUNT S D and HIGGINS T. Achieving real-time efficiency for adaptive radar pulse compression[C]. 2007 IEEE Radar Conference, Waltham, USA, 2007: 116–121. doi: 10.1109/RADAR.2007.374201.
    [11]
    BLUNT S D and HIGGINS T. Dimensionality reduction techniques for efficient adaptive pulse compression[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 349–362. doi: 10.1109/TAES.2010.5417167.
    [12]
    HIGGINS T, BLUNT S D, and GERLACH K. Gain-constrained adaptive pulse compression via an MVDR framework[C]. 2009 IEEE Radar Conference, Pasadena, USA, 2009: 1–6. doi: 10.1109/RADAR.2009.4977011.
    [13]
    BLUNT S D, SHACKELFORD A K, GERLACH K, et al. Doppler compensation & single pulse imaging using adaptive pulse compression[J]. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(2): 647–659. doi: 10.1109/TAES.2009.5089547.
    [14]
    CUPRAK T D and WAGE K E. Efficient Doppler-compensated reiterative minimum mean-squared-error processing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(2): 562–574. doi: 10.1109/TAES.2017.2651480.
    [15]
    AKHTAR J. Neural network LFM pulse compression[C]. 2023 IEEE Radar Conference, San Antonio, USA, 2023: 1–6. doi: 10.1109/RadarConf2351548.2023.10149646.
    [16]
    GAO Yvyang and DONG Ganggang. Pulse compression based on learnable matched filtering[C]. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024: 2568–2571. doi: 10.1109/IGARSS53475.2024.10640745.
    [17]
    李秀友, 董云龙, 黄勇, 等. 基于迭代线性约束最小方差的稳健自适应脉冲压缩方法[J]. 电子与信息学报, 2015, 37(10): 2300–2306. doi: 10.11999/JEIT141631.

    LI Xiuyou, DONG Yunlong, HUANG Yong, et al. Robust adaptive pulse compression algorithm based on reiterative linearly constrained minimum variance[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2300–2306. doi: 10.11999/JEIT141631.
    [18]
    裴家正, 黄勇, 陈宝欣, 等. 基于线性约束最小方差原则的稳健快速自适应脉冲压缩方法[J]. 系统工程与电子技术, 2022, 44(12): 3621–3630. doi: 10.12305/j.issn.1001-506X.2022.12.05.

    PEI Jiazheng, HUANG Yong, CHEN Baoxin, et al. Robust fast adaptive pulse compression method based on linearly constrained minimum variance principle[J]. Systems Engineering and Electronics, 2022, 44(12): 3621–3630. doi: 10.12305/j.issn.1001-506X.2022.12.05.
    [19]
    WANG Yongliang, CHEN Jianwen, BAO Zheng, et al. Robust space-time adaptive processing for airborne radar in nonhomogeneous clutter environments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(1): 71–81. doi: 10.1109/TAES.2003.1188894.
    [20]
    DIPIETRO R C. Extended factored space-time processing for airborne radar systems[C]. The 26th Asilomar Conference on Signals, Systems & Computers, Pacific Grove, USA, 1992: 425–430. doi: 10.1109/ACSSC.1992.269236.
    [21]
    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.
    [22]
    MELVIN W L and GUERCI J R. Knowledge-aided signal processing: A new paradigm for radar and other advanced sensors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 983–996. doi: 10.1109/TAES.2006.248215.
    [23]
    MELVIN W L and SHOWMAN G A. An approach to knowledge-aided covariance estimation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2006, 42(3): 1021–1042. doi: 10.1109/TAES.2006.248216.
    [24]
    YANG Zhaocheng, DE LAMARE R C, and LIU Weijian. Sparsity-based STAP using alternating direction method with gain/phase errors[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(6): 2756–2768. doi: 10.1109/TAES.2017.2714938.
    [25]
    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.
    [26]
    CUI Ning, XING Kun, YU Zhongjun, et al. Reduced-complexity subarray-level sparse recovery STAP for multichannel airborne radar WGMTI application[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(5): 6292–6313. doi: 10.1109/TAES.2023.3274104.
    [27]
    WANG Degen, WANG Tong, CUI Weichen, et al. A clutter suppression algorithm via enhanced sparse Bayesian learning for airborne radar[J]. IEEE Sensors Journal, 2023, 23(10): 10900–10911. doi: 10.1109/JSEN.2023.3263919.
    [28]
    DUAN Keqing, CHEN Hui, XIE Wenchong, et al. Deep learning for high-resolution estimation of clutter angle-Doppler spectrum in STAP[J]. IET Radar, Sonar & Navigation, 2022, 16(2): 193–207. doi: 10.1049/rsn2.12176.
    [29]
    VENKATASUBRAMANIAN S, WONGKAMTHONG C, SOLTANI M, et al. Toward data-driven stap radar[C]. 2022 IEEE Radar Conference (RadarConf22), New York City, USA, 2022: 1–5. doi: 10.1109/RadarConf2248738.2022.9764354.
    [30]
    LIU Jing, LIAO Guisheng, XU Jingwei, et al. Autoencoder neural network-based STAP algorithm for airborne radar with inadequate training samples[J]. Remote Sensing, 2022, 14(23): 6021. doi: 10.3390/rs14236021.
    [31]
    王俊, 郑彤, 雷鹏, 等. 深度学习在雷达中的研究综述[J]. 雷达学报, 2018, 7(4): 395–411. doi: 10.12000/JR18040.

    WANG Jun, ZHENG Tong, LEI Peng, et al. Study on deep learning in radar[J]. Journal of Radars, 2018, 7(4): 395–411. doi: 10.12000/JR18040.
    [32]
    RAJAN K and PATNAIK L M. Implementation of STAP algorithms on IBM SP2 and on ADSP 21062 dual digital signal processor systems[J]. Microprocessors and Microsystems, 2003, 27(4): 221–227. doi: 10.1016/S0141-9331(03)00022-X.
    [33]
    廖志鹏, 段克清, 何锦浚, 等. 基于可解释深度卷积网络的空时自适应处理方法[J]. 雷达学报, 2024, 13(4): 917–928. doi: 10.12000/JR24024.

    LIAO Zhipeng, DUAN Keqing, HE Jinjun, et al. Interpretable STAP algorithm based on deep convolutional neural network[J]. Journal of Radars, 2024, 13(4): 917–928. doi: 10.12000/JR24024.
    [34]
    ZOU Bo, WANG Xin, FENG Weike, et al. DU-CG-STAP method based on sparse recovery and unsupervised learning for airborne radar clutter suppression[J]. Remote Sensing, 2022, 14(14): 3472. doi: 10.3390/rs14143472.
    [35]
    朱晗归, 冯为可, 冯存前, 等. 机载雷达深度展开空时自适应处理方法[J]. 雷达学报, 2022, 11(4): 676–691. doi: 10.12000/JR22051.

    ZHU Hangui, FENG Weike, FENG Cunqian, et al. Deep unfolding based space-time adaptive processing method for airborne radar[J]. Journal of Radars, 2022, 11(4): 676–691. doi: 10.12000/JR22051.
    [36]
    LIAO Weikeng, CHOUDHARY A, WEINER D, et al. Design and evaluation of I/O strategies for parallel pipelined STAP applications[C]. 14th International Parallel and Distributed Processing Symposium, Cancun, Mexico, 2000: 655–662. doi: 10.1109/IPDPS.2000.846050.
    [37]
    LIAO Weikeng, CHOUDHARY A, WEINER D, et al. I/O implementation and evaluation of parallel pipelined STAP on high performance computers[C]. 6th International Conference on High Performance Computing, Calcutta, India, 1999: 354–358. doi: 10.1007/978-3-540-46642-0_51.
    [38]
    高飞, 陈辉, 任磊, 等. 一种新的机载雷达信号处理流程结构可行性证明及拓展性研究[J]. 信号处理, 2009, 25(11): 1785–1789. doi: 10.3969/j.issn.1003-0530.2009.11.023.

    GAO Fei, CHEN Hui, REN Lei, et al. Feasibility-proving of a new airborne radar signal processing architecture and extended research[J]. Journal of Signal Processing, 2009, 25(11): 1785–1789. doi: 10.3969/j.issn.1003-0530.2009.11.023.
    [39]
    YANG Yan, SUN Jian, LI Huibin, et al. ADMM-CSNet: A deep learning approach for image compressive sensing[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(3): 521–538. doi: 10.1109/TPAMI.2018.2883941.
    [40]
    MA Jiawei, LIU Xiaoyang, SHOU Zheng, et al. Deep tensor ADMM-net for snapshot compressive imaging[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea (South), 2019: 10222–10231. doi: 10.1109/ICCV.2019.01032.
    [41]
    MARQUES E C, MACIEL N, NAVINER L, et al. Deep learning approaches for sparse recovery in compressive sensing[C]. 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), Dubrovnik, Croatia, 2019: 129–134. doi: 10.1109/ISPA.2019.8868841.
    [42]
    ZHANG Kai, VAN GOOL L, and TIMOFTE R. Deep unfolding network for image super-resolution[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 3214–3223. doi: 10.1109/CVPR42600.2020.00328.
    [43]
    BEN SAHEL Y, BRYAN J P, CLEARY B, et al. Deep unrolled recovery in sparse biological imaging: Achieving fast, accurate results[J]. IEEE Signal Processing Magazine, 2022, 39(2): 45–57. doi: 10.1109/MSP.2021.3129995.
    [44]
    TAN Xiao, YANG Zhiwei, LI Xianghai, et al. Gridless sparse recovery STAP algorithm with array amplitude-phase errors for non-uniform linear array[J]. Digital Signal Processing, 2024, 144: 104296. doi: 10.1016/j.dsp.2023.104296.
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