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YU Lei, YU Ruofeng, HE Feng, et al. Deep learning-based integrated search-imaging waveform design for coherent frequency diverse array radar[J]. Journal of Radars, in press. doi: 10.12000/JR25127
Citation: YU Lei, YU Ruofeng, HE Feng, et al. Deep learning-based integrated search-imaging waveform design for coherent frequency diverse array radar[J]. Journal of Radars, in press. doi: 10.12000/JR25127
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Deep Learning-based Integrated Search-imaging Waveform Design for Coherent Frequency Diverse Array Radar

DOI: 10.12000/JR25127 CSTR: 32380.14.JR25127

  • College of Electronics Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  Innovation Research Foundation of NUDT (ZK25-28)
More Information
  • Corresponding author: HE Feng, hefeng@nudt.edu.cn
  • Received Date: 2025-07-21
  • Rev Recd Date: 2025-12-25
    • Available Online: 2025-12-29
    Abstract
  • Coherent Frequency Diverse Array (FDA) radar demonstrates significant potential for wide-area search tasks due to its simple system architecture, flexible beam scanning, and high transmit Degrees of Freedom (DOF). However, its inherent beam-scanning mechanism reduces dwell time in specific directions, thereby limiting the imaging range resolution when a conventional wideband waveform is used. To resolve the intrinsic contradiction between wide-area search and high-resolution imaging, this paper proposes a deep learning-based integrated search-imaging waveform design method. By leveraging the multi-DoF flexible transmission capability of coherent FDA, the proposed method customizes multidimensional transmit resources, including waveform, bandwidth, and transmit gain, for multiple Regions of Interest (ROIs) while preserving wide-coverage search performance. To address the nonconvex optimization problem with dual constraints of constant modulus and low correlation in baseband waveform design, a residual autoencoder-based optimizer is developed. This network directly learns and establishes a high-dimensional nonlinear mapping from the initial phase space to the optimized phase space that satisfies predefined performance criteria. The network efficiently generates a set of phase-coded subwaveforms exhibiting low autocorrelation sidelobes and low cross-correlation levels for multiple ROIs. Simulation results validate the effectiveness of this method, demonstrating that the designed waveforms achieve higher processing gain (compared with the narrowband searching mode) and improved imaging resolution in the designated ROIs during simultaneous search and multitarget imaging. Moreover, the autocorrelation and cross-correlation performance of the proposed method significantly outperforms that of conventional approaches, indicating that it provides an effective solution for enhancing the multitask capabilities of modern radar systems.

     

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    • References(25)
  • [1]
    吴曼青, 赵逸超, 何峰, 等. 计算阵列-计算赋能的数字阵列技术[J]. 中国科学: 信息科学, 2022, 52(12): 2270–2289. doi: 10.1360/SSI-2021-0368.

    WU Manqing, ZHAO Yichao, HE Feng, et al. Computational array-digital array with computational empowerment[J]. SCIENTIA SINICA Informationis, 2022, 52(12): 2270–2289. doi: 10.1360/SSI-2021-0368.
    [2]
    郭瑞, 张月, 田彪, 等. 全息凝视雷达系统技术与发展应用综述[J]. 雷达学报, 2023, 12(2): 389–411. doi: 10.12000/JR22153.

    GUO Rui, ZHANG Yue, TIAN Biao, et al. Review of the technology, development and applications of holographic staring radar[J]. Journal of Radars, 2023, 12(2): 389–411. doi: 10.12000/JR22153.
    [3]
    YU Lei, HE Feng, and SU Yi. Multiscale observation in wide-spatial radar surveillance based on coherent FDA design[J]. Science China Information Sciences, 2024, 67(2): 122304. doi: 10.1007/s11432-022-3816-3.
    [4]
    ANTONIK P, WICKS W, GRIFFITHS H, et al. Frequency diverse array radars[C]. 2006 IEEE Conference on Radar, Verona, USA, 2006: 470–475. doi: 10.1109/RADAR.2006.1631800.
    [5]
    许京伟, 朱圣棋, 廖桂生, 等. 频率分集阵雷达技术探讨[J]. 雷达学报, 2018, 7(2): 167–182. doi: 10.12000/JR18023.

    XU Jingwei, ZHU Shengqi, LIAO Guisheng, et al. An overview of frequency diverse array radar technology[J]. Journal of Radars, 2018, 7(2): 167–182. doi: 10.12000/JR18023.
    [6]
    桂荣华. 频控阵雷达自适应处理关键技术研究[D]. [博士论文], 电子科技大学, 2020. doi: 10.27005/d.cnki.gdzku.2020.000097.

    GUI Ronghua. Research on adaptive processing technology for frequency diverse array radar[D]. [Ph.D. dissertation], University of Electronic Science and Technology of China, 2020. doi: 10.27005/d.cnki.gdzku.2020.000097.
    [7]
    许京伟, 兰岚, 朱圣棋, 等. 相干频率分集阵雷达匹配滤波器设计[J]. 系统工程与电子技术, 2018, 40(8): 1720–1728. doi: 10.3969/j.issn.1001-506X.2018.08.08.

    XU Jingwei, LAN Lan, ZHU Shengqi, et al. Design of matched filter for coherent FDA radar[J]. Systems Engineering and Electronics, 2018, 40(8): 1720–1728. doi: 10.3969/j.issn.1001-506X.2018.08.08.
    [8]
    于雷, 何峰, 董臻, 等. 一种基于非线性调频信号和空域编码的FDA雷达波形设计方法[J]. 雷达学报, 2021, 10(6): 822–832. doi: 10.12000/JR21008.

    YU Lei, HE Feng, DONG Zhen, et al. A waveform design method based on nonlinear frequency modulation and space-coding for coherent frequency diverse array radar[J]. Journal of Radars, 2021, 10(6): 822–832. doi: 10.12000/JR21008.
    [9]
    WANG Huake, LIAO Guisheng, XU Jingwei, et al. Transmit beampattern design for coherent FDA by piecewise LFM waveform[J]. Signal Processing, 2019, 161: 14–24. doi: 10.1016/j.sigpro.2019.03.010.
    [10]
    YU Lei, HE Feng, SU Yi, et al. Transmitting strategy with high degrees of freedom for pulsed-coherent FDA radar[J]. IET Radar, Sonar & Navigation, 2022, 16(4): 659–667. doi: 10.1049/rsn2.12210.
    [11]
    王华柯. 新型发射分集MIMO雷达方向图设计及信号处理研究[D]. [博士论文], 西安电子科技大学, 2021. doi: 10.27389/d.cnki.gxadu.2021.000001.

    WANG Huake. Research on beampattern design and signal processing for transmit diversity MIMO radar[D]. [Ph.D. dissertation], Xidian University, 2021. doi: 10.27389/d.cnki.gxadu.2021.000001.
    [12]
    WANG Huake, LIAO Guisheng, XU Jingwei, et al. Space-time matched filter design for interference suppression in coherent frequency diverse array[J]. IET Signal Processing, 2020, 14(3): 175–181. doi: 10.1049/iet-spr.2019.0227.
    [13]
    YU Lei, ZHANG Qilei, HE Feng, et al. A controllable range resolution enhancement method for coherent FDA radar[C]. 2021 CIE International Conference on Radar (Radar), Haikou, China, 2021: 1963–1967. doi: 10.1109/Radar53847.2021.10028540.
    [14]
    YU Lei, HE Feng, ZHANG Yongsheng, et al. Low-PSL mismatched filter design for coherent FDA radar using phase-coded waveform[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 3507405. doi: 10.1109/LGRS.2023.3309827.
    [15]
    QIU Xiangfeng, JIANG Weidong, ZHANG Xinyu, et al. Simultaneous design of PCFM waveforms and receive filters toward ISRJ suppression[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 3509305. doi: 10.1109/LGRS.2024.3457536.
    [16]
    LI Yongzhe and VOROBYOV S A. Fast algorithms for designing unimodular waveform(s) with good correlation properties[J]. IEEE Transactions on Signal Processing, 2018, 66(5): 1197–1212. doi: 10.1109/TSP.2017.2787104.
    [17]
    KANG B, KWEON J, RANGASWAMY M, et al. Deep learning for radar waveform design: Retrospectives and the road ahead[C]. 2023 IEEE International Radar Conference (RADAR), Sydney, Australia, 2023: 1–6. doi: 10.1109/RADAR54928.2023.10371126.
    [18]
    ZHAO Ziwei, HU Jinfeng, ZHONG Kai, et al. MIMO radar waveform design for range-ISL optimization via iterative deep unfolding network[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 3503405. doi: 10.1109/LGRS.2024.3368446.
    [19]
    SEKIYA R, MORI H, HASHIMOTO H, et al. Design of long-sequence unimodular waveforms using an original autoencoder for MIMO radar systems[C]. 2023 20th European Radar Conference (EuRAD), Berlin, Germany, 2023: 339–342. doi: 10.23919/EuRAD58043.2023.10289508.
    [20]
    SEKIYA R, MORI H, HASHIMOTO H, et al. Use of ResNet autoencoders for designing phase-quantized sequences with good correlation for MIMO radar systems[J]. IEEE Transactions on Radar Systems, 2025, 3: 681–694. doi: 10.1109/TRS.2025.3562698.
    [21]
    HU Jinfeng, WEI Zhiyong, LI Yuzhi, et al. Designing unimodular waveform(s) for MIMO radar by deep learning method[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(2): 1184–1196. doi: 10.1109/TAES.2020.3037406.
    [22]
    GUI Ronghua, WANG Wenqin, CUI Can, et al. Coherent pulsed-FDA radar receiver design with time-variance consideration: SINR and CRB analysis[J]. IEEE Transactions on Signal Processing, 2018, 66(1): 200–214. doi: 10.1109/TSP.2017.2764860.
    [23]
    BARTON D K. Low-angle radar tracking[J]. Proceedings of the IEEE, 1974, 62(6): 687–704. doi: 10.1109/PROC.1974.9509.
    [24]
    HE Hao, STOICA P, and LI Jian. Designing unimodular sequence sets with good correlations-including an application to MIMO radar[J]. IEEE Transactions on Signal Processing, 2009, 57(11): 4391–4405. doi: 10.1109/TSP.2009.2025108.
    [25]
    ZHANG Ben, ZHANG Yongsheng, HE Feng, et al. Range resolution control and fractional Fourier domain target localization with nonlinear time-variant phase diverse FDA[J]. IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(6): 17151–17171. doi: 10.1109/TAES.2025.3601572.
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