Volume 14 Issue 2
Apr.  2025
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2025  >  14(2): 293-308
WANG Bohong, SHEN Biao, MU Wenxing, et al. Research on super-resolution methods for radar targets based on bat-inspired spectrogram correlation and transformation models[J]. Journal of Radars, 2025, 14(2): 293–308. doi: 10.12000/JR24239
Citation: WANG Bohong, SHEN Biao, MU Wenxing, et al. Research on super-resolution methods for radar targets based on bat-inspired spectrogram correlation and transformation models[J]. Journal of Radars, 2025, 14(2): 293–308. doi: 10.12000/JR24239
shu

Research on Super-resolution Methods for Radar Targets Based on Bat-inspired Spectrogram Correlation and Transformation Models

DOI: 10.12000/JR24239 CSTR: 32380.14.JR24239

  • School of Electronic Engineering, Naval University of Engineering, Wuhan 430033, China

Funds:  The National Natural Science Foundation of China (62171452)
More Information
  • Corresponding author: LIU Tao, liutao1018@sina.com
  • Received Date: 2024-12-01
  • Rev Recd Date: 2025-02-19
    • Available Online: 2025-02-22
  • Publish Date: 2025-03-17
    Abstract
  • The resolving power of traditional radar is mainly analyzed using the ambiguity function, and its limit is generally characterized by the Rayleigh limit. Bats have a rather sensitive auditory system. Researchers have proposed the Spectrogram Correlation And Transformation (SCAT) model to represent the auditory system of bats, explored their super-resolution principle, and provided a possible means to break through the conventional (Rayleigh) resolving power limit of radar targets. To further enhance the discriminative performance of the SCAT model, two bat-auditory-system-based super-resolution models, namely the base vector deconvolution method and Baseband SCAT (BSCT), are improved by suppressing redundant wave flaps at the negative semiaxis of the range profile and at the origin. Meanwhile, the concept and computation method of reliable discriminative power are proposed to unify the measurements of SCAT and Rayleigh discriminative powers. Further, a comparison is made to validate the rationality of the concept of reliable discriminative power, and the effectiveness of the improved models is verified. Simulation and real experiments show that the improved super-resolution models achieve a sizable increase in the resolving power. Notably, the improved base vector deconvolution method performs the best, improving the resolving power of the original method by ～2 dB while enhancing the matched filtering resolving power by ～5 dB.

     

    • FullText(HTML)
  • loading
    • References(34)
  • [1]
    陈岁新. 雷达群目标超分辨技术研究[D]. [硕士论文], 电子科技大学, 2022: 1–5. doi: 10.27005/d.cnki.gdzku.2022.001266.

    CHEN Suixin. Study on super-resolution for radar detection of group targets[D]. [Master dissertation], University of Electronic Science and Technology of China, 2022: 1–5. doi: 10.27005/d.cnki.gdzku.2022.001266.
    [2]
    FENG Junjie, SUN Yinan, and JI Xiuxia. High-resolution ISAR imaging based on improved sparse signal recovery algorithm[J]. Wireless Communications and Mobile Computing, 2021, 2021: 5541116. doi: 10.1155/2021/5541116.
    [3]
    倪晋麟, 储晓彬, 林幼权. 基于去卷积距离超分辨方法的机理及限制条件[J]. 系统工程与电子技术, 2000, 22(3): 62–64. doi: 10.3321/j.issn:1001-506X.2000.03.019.

    NI Jinlin, CHU Xiaobin, and LIN Youquan. The principle and limitation of the range super-resolution algorithms based on deconvolution[J]. Systems Engineering and Electronics, 2000, 22(3): 62–64. doi: 10.3321/j.issn:1001-506X.2000.03.019.
    [4]
    王永良. 空间谱估计理论与算法[M]. 北京: 清华大学出版社, 2004: 18–52, 306–336.

    WANG Yongliang. Spatial Spectrum Estimation Theory and Algorithms[M]. Beijing: Tsinghua University Press, 2004: 18–52, 306–336.
    [5]
    DING Shanshan, TONG Ningning, ZHANG Yongshun, et al. Super-resolution 3D imaging in MIMO radar using spectrum estimation theory[J]. IET Radar, Sonar & Navigation, 2017, 11(2): 304–312. doi: 10.1049/iet-rsn.2016.0233.
    [6]
    王璟琛. 密集群目标分辨方法研究[D]. [硕士论文], 西安电子科技大学, 2020. doi: 10.27389/d.cnki.gxadu.2020.001665.

    WANG Jingchen. Research on target discrimination method of dense cluster[D]. [Master dissertation], Xidian University, 2020. doi: 10.27389/d.cnki.gxadu.2020.001665.
    [7]
    陈希信. 基于LFM信号频域去斜和压缩感知的雷达距离超分辨[J]. 现代雷达, 2022, 44(12): 70–73. doi: 10.16592/j.cnki.1004-7859.2022.12.010.

    CHEN Xixin. Radar range super-resolution based on LFM frequency dechirp and compressive sensing[J]. Modern Radar, 2022, 44(12): 70–73. doi: 10.16592/j.cnki.1004-7859.2022.12.010.
    [8]
    WEI Shunjun, ZHOU Zichen, WANG Mou, et al. 3DRIED: A high-resolution 3-D millimeter-wave radar dataset dedicated to imaging and evaluation[J]. Remote Sensing, 2021, 13(17): 3366. doi: 10.3390/rs13173366.
    [9]
    康乐, 张群, 李涛泳, 等. 基于贝叶斯学习的下视三维合成孔径雷达成像方法[J]. 光学学报, 2017, 37(6): 0611003. doi: 10.3788/AOS201737.0611003.

    KANG Le, ZHANG Qun, LI Taoyong, et al. Imaging method of downward-looking three-dimensional synthetic aperture radar based on bayesian learning[J]. Acta Optica Sinica, 2017, 37(6): 0611003. doi: 10.3788/AOS201737.0611003.
    [10]
    SIMMONS J A, FERRAGAMO M, MOSS C F, et al. Discrimination of jittered sonar echoes by the echolocating bat, Eptesicus fuscus: The shape of target images in echolocation[J]. Journal of Comparative Physiology A, 1990, 167(5): 589–616. doi: 10.1007/BF00192654.
    [11]
    SIMMONS J A, SAILLANT P A, WOTTON J M, et al. Composition of biosonar images for target recognition by echolocating bats[J]. Neural Networks, 1995, 8(7/8): 1239–1261. doi: 10.1016/0893-6080(95)00059-3.
    [12]
    SCHMIDT S. Perception of structured phantom targets in the echolocating bat, Megaderma lyra[J]. The Journal of the Acoustical Society of America, 1992, 91(4): 2203–2223. doi: 10.1121/1.403654.
    [13]
    杨琳. 镫骨、耳蜗及其Corti器的建模与生物力学研究[D]. [博士论文], 复旦大学, 2009: 17–34. doi: 10.7666/d.y1970550.

    YANG Lin. Modeling and biomechanical analysis of the stapes, cochlea and organ of Corti[D]. [Ph.D. dissertation], Fudan University, 2009: 17–34. doi: 10.7666/d.y1970550.
    [14]
    CHI T, RU Powen, and SHAMMA S A. Multiresolution spectrotemporal analysis of complex sounds[J]. The Journal of the Acoustical Society of America, 2005, 118(2): 887–906. doi: 10.1121/1.1945807.
    [15]
    秦晓瑜. 基于听觉仿生的听觉谱生成方法研究[D]. [硕士论文], 东北师范大学, 2013: 1–18.

    QIN Xiaoyu. Study on the generation method of auditory spectrum based on auditory bionics[D]. [Master dissertation], Northeast Normal University, 2013: 1–18.
    [16]
    SAILLANT P A, SIMMONS J A, DEAR S P, et al. A computational model of echo processing and acoustic imaging in frequency-modulated echolocating bats: The spectrogram correlation and transformation receiver[J]. The Journal of the Acoustical Society of America, 1993, 94(5): 2691–2712. doi: 10.1121/1.407353.
    [17]
    MATSUO I, KUNUGIYAMA K, and YANO M. An echolocation model for range discrimination of multiple closely spaced objects: Transformation of spectrogram into the reflected intensity distribution[J]. The Journal of the Acoustical Society of America, 2004, 115(2): 920–928. doi: 10.1121/1.1642626.
    [18]
    MATSUO I and YANO M. An echolocation model for the restoration of an acoustic image from a single-emission echo[J]. The Journal of the Acoustical Society of America, 2004, 116(6): 3782–3788. doi: 10.1121/1.1811411.
    [19]
    WIEGREBE L. An autocorrelation model of bat sonar[J]. Biological Cybernetics, 2008, 98(6): 587–595. doi: 10.1007/s00422-008-0216-2.
    [20]
    PEREMANS H and HALLAM J. The spectrogram correlation and transformation receiver, revisited[J]. The Journal of the Acoustical Society of America, 1998, 104(2): 1101–1110. doi: 10.1121/1.423326.
    [21]
    SIMON R, KNÖRNSCHILD M, TSCHAPKA M, et al. Biosonar resolving power: Echo-acoustic perception of surface structures in the submillimeter range[J]. Frontiers in Physiology, 2014, 5: 64. doi: 10.3389/fphys.2014.00064.
    [22]
    PARK M and ALLEN R. Pattern-matching analysis of fine echo delays by the spectrogram correlation and transformation receiver[J]. The Journal of the Acoustical Society of America, 2010, 128(3): 1490–1500. doi: 10.1121/1.3466844.
    [23]
    SIMMONS J A, SAILLANT P A, FERRAGAMO M J, et al. Auditory Computations for Biosonar Target Imaging in Bats[M]. HAWKINS H L, MCMULLEN T A, POPPER A N, et al. Auditory Computation. New York: Springer, 1996: 401–468. doi: 10.1007/978-1-4612-4070-9_9.
    [24]
    GEORGIEV K, BALLERI A, STOVE A, et al. Baseband version of the bat-inspired spectrogram correlation and transformation receiver[C]. 2016 IEEE Radar Conference, Philadelphia, PA, USA, 2016: 1–6. doi: 10.1109/RADAR.2016.7485152.
    [25]
    GEORGIEV K, BALLERI A, STOVE A, et al. Bio-inspired two target resolution at radio frequencies[C]. 2017 IEEE Radar Conference, Seattle, WA, USA, 2017: 436–440. doi: 10.1109/RADAR.2017.7944242.
    [26]
    GEORGIEV K, BALLERI A, STOVE A, et al. Bio-inspired processing of radar target echoes[J]. IET Radar, Sonar & Navigation, 2018, 12(12): 1402–1409. doi: 10.1049/iet-rsn.2018.5241.
    [27]
    成彬彬. 自适应雷达波形的仿生处理研究[D]. [博士论文], 清华大学, 2009.

    CHENG Binbin. Research on bionic processing for auto-adaptive radar waveform[D]. [Ph.D. dissertation], Tsinghua University, 2009.
    [28]
    苏梦娜, 梁红, 杨长生. 基于SCAT模型的水下多目标高分辨仿生成像方法[J]. 水下无人系统学报, 2019, 27(2): 189–193. doi: 10.11993/j.issn.2096-1509.2019.02.010.

    SU Mengna, LIANG Hong, and YANG Changsheng. Bionic imaging of underwater multiple targets with high resolution based on SCAT model[J]. Journal of Unmanned Undersea Systems, 2019, 27(2): 189–193. doi: 10.11993/j.issn.2096-1509.2019.02.010.
    [29]
    CHEN Ming, BATES M E, and SIMMONS J A. How frequency hopping suppresses pulse-echo ambiguity in bat biosonar[J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(29): 17288–17295. doi: 10.1073/pnas.2001105117.
    [30]
    CHEN Ming, HARO S, SIMMONS A M, et al. A comprehensive computational model of animal biosonar signal processing[J]. PLOS Computational Biology, 2021, 17(2): e1008677. doi: 10.1371/journal.pcbi.1008677.
    [31]
    BALLERI A, GRIFFITHS H, and BAKER C. Biologically-Inspired Radar and Sonar: Lessons from Nature[M]. Edison: SciTech Publishing, 2017: 1–81.
    [32]
    丁鹭飞, 耿富禄, 陈建春. 雷达原理[M]. 6版. 北京: 电子工业出版社, 2020: 210–211, 348–360.

    DING Lufei, GENG Fulu, and CHEN Jianchun. Radar Principles[M]. 6th ed. Beijing: Publishing House of Electronics Industry, 2020: 210–211, 348–360.
    [33]
    王罗胜斌, 王雪松, 徐振海. 雷达极化域调控超分辨的原理与方法[J]. 中国科学: 信息科学, 2023, 53(5): 993–1007. doi: 10.1360/SSI-2022-0141.

    WANG Luoshengbin, WANG Xuesong, and XU Zhenhai. Principle and approach to polarization modulation for radar super-resolution[J]. SCIENTIA SINICA Informationis, 2023, 53(5): 993–1007. doi: 10.1360/SSI-2022-0141.
    [34]
    WANG Luoshengbin, XU Zhenhai, DONG Wei, et al. A scheme of polarimetric superresolution for multitarget detection and localization[J]. IEEE Signal Processing Letters, 2021, 28: 439–443. doi: 10.1109/LSP.2021.3058007.
    • 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(509) PDF downloads(162) Cited by()
    Proportional views
    Related

    京ICP备20021838号-14   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return