深度学习在雷达中的研究综述

王俊 郑彤 雷鹏 魏少明

王俊, 郑彤, 雷鹏, 魏少明. 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
引用本文: 王俊, 郑彤, 雷鹏, 魏少明. 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, Wei Shaoming. Study on Deep Learning in Radar[J]. Journal of Radars, 2018, 7(4): 395-411. doi: 10.12000/JR18040
Citation: Wang Jun, Zheng Tong, Lei Peng, Wei Shaoming. Study on Deep Learning in Radar[J]. Journal of Radars, 2018, 7(4): 395-411. doi: 10.12000/JR18040

深度学习在雷达中的研究综述

doi: 10.12000/JR18040
基金项目: 国家自然科学基金(61501011,61671035)
详细信息
    作者简介:

    王 俊(1972–),男,教授。现于北京航空航天大学电子信息工程学院从事科研教学工作。1995年于西北工业大学获通信工程专业工学学士学位,1998年、2001年于北京航空航天大学分别获信号与信息处理专业工学硕士学位、信号与信息处理专业工学博士学位。现为中国电子学会高级会员。研究方向为雷达信号处理、FPGA/DSP嵌入式系统、目标识别与跟踪。E-mail: wangj203@buaa.edu.cn

    郑 彤(1991–),女,博士生。分别于2014年及2017年获北方工业大学电子信息工程专业学士、信息与通信工程专业硕士学位。现攻读北京航空航天大学信号与信息处理专业博士学位。主要研究方向为信号处理、模式识别。 

    雷 鹏(1985–),男,2006年获北京航空航天大学电子信息工程专业学士学位,2012年获北京航空航天大学信号与信息处理专业博士学位。现任北京航空航天大学电子信息工程学院讲师。主要研究领域为数字信号与图像处理、目标识别

    魏少明(1985–),男,实验师。现于北京航空航天大学电子信息工程学院从事科研与实验教学工作。2007年于北京航空航天大学获通信工程专业工学学士学位,2014年于北京航空航天大学获信号与信息处理专业工学博士学位。现为中国电子学会会员。研究方向为雷达信号处理、多目标跟踪、3维成像

    通讯作者:

    王俊  wangj203@buaa.edu.cn

Study on Deep Learning in Radar

Funds: The National Natural Science Foundation of China (61501011, 61671035)
  • 摘要: 雷达通过发射天线发射电磁波,经过不同物体反射接收到相应的反射波,对其接收结果进行分析,能得到物体距雷达的位置,径向运动速度等信息,所以对雷达信号的分析具有重要的研究意义。近些年深度学习成为各个领域的研究热点,而在雷达领域同样可通过深度学习算法实现对信号的相应的信息处理。与传统方法相比,深度学习算法具有自动提取深层特征、获取较高准确率等优势。该文具体介绍了近期典型的深度学习算法在雷达信号处理中的应用及研究情况。此外,该文介绍了两个在雷达领域中应用深度学习亟待解决的问题,即过拟合和可解译性。

     

  • 图  1  本文介绍流程

    Figure  1.  Flow chart of this paper

    图  2  CNN结构示意图

    Figure  2.  Typical CNN structure

    图  3  AE结构示意图

    Figure  3.  Typical AE structure

    图  4  DBN结构示意图

    Figure  4.  Typical DBN structure

    图  5  MSTAR数据示意图

    Figure  5.  Illustration of MSTAR data

    图  6  4种目标HRRP示意图

    Figure  6.  HRRPs of four targets

    图  7  两仿真目标时频谱图

    Figure  7.  Time-frequency map pf two simulation targets

    图  8  4种手势R-D谱图

    Figure  8.  R-D map of four gestures

    图  9  两种检测网络架构

    Figure  9.  Two detection structure

  • [1] Casagli N, Catani F, Del Ventisette C, et al. Monitoring, prediction, and early warning using ground-based radar interferometry[J]. Landslides, 2010, 7(3): 291–301. DOI: 10.1007/s10346-010-0215-y
    [2] Dzvonkovskaya A. HF surface wave radar for tsunami alerting: From system concept and simulations to integration into early warning systems[J]. IEEE Aerospace and Electronic Systems Magazine, 2018, 33(3): 48–58. DOI: 10.1109/MAES.2018.160267
    [3] Baer G, Magen Y, Nof R N, et al. InSAR measurements and viscoelastic modeling of sinkhole precursory subsidence: Implications for sinkhole formation, early warning, and sediment properties[J]. Journal of Geophysical Research:Earth Surface, 2018, 123(4): 678–693. DOI: 10.1002/jgrf.v123.4
    [4] Beasley J E, Howells H, and Sonander J. Improving short-term conflict alert via tabu search[J]. Journal of the Operational Research Society, 2002, 53(6): 593–602. DOI: 10.1057/palgrave.jors.2601358
    [5] 周游, 任伦, 李硕. 基于ADS_B的警戒搜索雷达空情过滤方法[J]. 火控雷达技术, 2018, 47(1): 21–23, 31

    Zhou You, Ren Lun, and Li Shuo. Method of warning search radar air situation filtering based on ADS_B[J]. Fire Control Radar Technology, 2018, 47(1): 21–23, 31
    [6] Lorente P, Varela S P, Soto-Navarro J, et al. The high-frequency coastal radar network operated by puertos del estado (Spain): Roadmap to a fully operational implementation[J]. IEEE Journal of Oceanic Engineering, 2017, 42(1): 56–72.
    [7] Borge J C N and Soares C G. Analysis of directional wave fields using X-band navigation radar[J]. Coastal Engineering, 2000, 40(4): 375–391. DOI: 10.1016/S0378-3839(00)00019-3
    [8] Huang H and Wang W Q. FDA-OFDM for integrated navigation, sensing, and communication systems[J]. IEEE Aerospace and Electronic Systems Magazine, 2018, 33(5/6): 34–42.
    [9] Scannapieco A F, Renga A, Fasano G, et al. Experimental analysis of radar odometry by commercial ultralight radar sensor for miniaturized UAS[J]. Journal of Intelligent&Robotic Systems, 2018, 90(3/4): 485–503.
    [10] Bao X H, Luo Y L, Sun J X, et al. Assimilating Doppler radar observations with an ensemble Kalman filter for convection-permitting prediction of convective development in a heavy rainfall event during the pre-summer rainy season of South China[J]. Science China Earth Sciences, 2017, 60(10): 1866–1885. DOI: 10.1007/s11430-017-9076-9
    [11] Orzel K A and Frasier S J. Weather observation by an electronically scanned dual-polarization phase-tilt radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(5): 2722–2734. DOI: 10.1109/TGRS.2017.2782480
    [12] Li N, Wang Z H, Sun K Y, et al. A quality control method of ground-based weather radar data based on statistics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2211–2219. DOI: 10.1109/TGRS.2017.2776562
    [13] 施岩龙, 黄柏圣, 晏靖靖, 等. 基于组网雷达的探测资源调配研究[J]. 现代雷达, 2017, 36(6): 12–15, 22. DOI: 10.16592/j.cnki.1004-7859.2017.06.003

    Shi Yan-long, Huang Bai-sheng, Yan Jing-jing, et al. A study on detection resource management based on radar network[J]. Modern Radar, 2017, 36(6): 12–15, 22. DOI: 10.16592/j.cnki.1004-7859.2017.06.003
    [14] Maury S, Tiwari R K, and Balaji S. Joint application of satellite remote sensing, ground penetrating radar (GPR) and resistivity techniques for targeting ground water in fractured Ophiolites of South Andaman Island, India[J]. Environmental Earth Sciences, 2016, 75(3): 237. DOI: 10.1007/s12665-015-5007-1
    [15] Dhakate R, Amarender B, Kumar V S, et al. Application of ground-penetrating radar for identification of groundwater resources in a coastal terrain[J]. Arabian Journal of Geosciences, 2015, 8(7): 4703–4715. DOI: 10.1007/s12517-014-1567-8
    [16] Sulistioadi Y B, Tseng K H, Shum C K, et al. Satellite radar altimetry for monitoring small rivers and lakes in Indonesia[J].Hydrology and Earth System Sciences Discussions, 2014, 11: 2825–2874. DOI: 10.5194/hessd-11-2825-2014
    [17] Melo S, Maresca S, Pinna S, et al. Photonics-based dual-band radar for Landslides monitoring in presence of multiple scatterers[J]. Journal of Lightwave Technology, 2018, 36(12): 2337–2343. DOI: 10.1109/JLT.2018.2814638
    [18] 徐方. 环境监测对环境治理的促进性作用[J]. 环境与发展, 2018, 30(1): 133, 136

    Xu F. Environmental monitoring on the promotion of environmental governance role[J]. Environment and Development, 2018, 30(1): 133, 136
    [19] Rosenkrantz A B, Verma S, Choyke P, et al. Prostate magnetic resonance imaging and magnetic resonance imaging targeted biopsy in patients with a prior negative biopsy: A consensus statement by AUA and SAR[J]. Journal of Urology, 2016, 196(6): 1613–1618. DOI: 10.1016/j.juro.2016.06.079
    [20] Confuorto P, Di Martire D, Centolanza G, et al. Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis[J]. Remote Sensing of Environment, 2017, 188: 51–72. DOI: 10.1016/j.rse.2016.11.002
    [21] Zhang G S, Perrie W, Li X F, et al. A hurricane morphology and sea surface wind vector estimation model based on C-band cross-polarization SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1743–1751. DOI: 10.1109/TGRS.2016.2631663
    [22] Deledalle C A, Denis L, Tupin F, et al. NL-SAR: A unified nonlocal framework for resolution-preserving (Pol)(In)SAR denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2021–2038. DOI: 10.1109/TGRS.2014.2352555
    [23] Dellinger F, Delon J, Gousseau Y, et al. SAR-SIFT: A SIFT-like algorithm for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 453–466. DOI: 10.1109/TGRS.2014.2323552
    [24] Krieger G. MIMO-SAR: Opportunities and pitfalls[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2628–2645. DOI: 10.1109/TGRS.2013.2263934
    [25] Wang C, Xia H Y, Liu Y P, et al. Spatial resolution enhancement of coherent Doppler wind lidar using joint time-frequency analysis[J]. Optics Communications, 2018, 424: 48–53. DOI: 10.1016/j.optcom.2018.04.042
    [26] Ai X F, Wang L D, Wang M X, et al.. Bistatic high-range resolution profiles of wobbling targets[C]. Proceedings of IET International Radar Conference 2015, Hangzhou, China, 2015: 1–4
    [27] Berndt R J. Aircraft micro-Doppler feature extraction from high range resolution profiles[C]. Proceedings of 2015 IEEE Radar Conference, Johannesburg, South Africa, 2015: 457–462
    [28] Kim K T. Focusing of high range resolution profiles of moving targets using stepped frequency waveforms[J]. IET Radar,Sonar&Navigation, 2010, 4(4): 564–575.
    [29] Du R, Fan Y Y, and Wang J S. Pedestrian and bicyclist identification through micro Doppler signature with different approaching aspect angles[J]. IEEE Sensors Journal, 2018, 18(9): 3827–3835. DOI: 10.1109/JSEN.2018.2816594
    [30] Li G, Zhang R, Ritchie M, et al. Sparsity-driven micro-Doppler feature extraction for dynamic hand gesture recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(2): 655–665. DOI: 10.1109/TAES.2017.2761229
    [31] Ji J Z, Jiang J X, Al-Armaghany A, et al. Nutation and geometrical parameters estimation of cone-shaped target based on micro-Doppler effect[J]. Optik-International Journal for Light and Electron Optics, 2017, 150: 1–10. DOI: 10.1016/j.ijleo.2017.09.087
    [32] Singh A K and Kim Y H. Automatic measurement of blade length and rotation rate of drone using W-band micro-Doppler radar[J]. IEEE Sensors Journal, 2018, 18(5): 1895–1902. DOI: 10.1109/JSEN.2017.2785335
    [33] Quan Y H, Wu Y J, Li Y C, et al. Range-Doppler reconstruction for frequency agile and PRF-jittering radar[J]. IET Radar,Sonar&Navigation, 2018, 12(3): 348–352.
    [34] Gui R H, Wang W Q, Pan Y, et al. Cognitive target tracking via angle-range-Doppler estimation with transmit subaperturing FDA radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 76–89. DOI: 10.1109/JSTSP.2018.2793761
    [35] Kesaraju S, Mathews J D, Milla M, et al. Range-Doppler mapping of space-based targets using the JRO 50 MHz radar[J]. Earth,Moon,and Planets, 2017, 120(3): 169–188. DOI: 10.1007/s11038-017-9510-0
    [36] Wang Y K, Xiao Z L, Wu L, et al. Jittered Chirp sequence waveform in combination with CS-based unambiguous Doppler processing for automotive frequency-modulated continuous wave radar[J]. IET Radar,Sonar&Navigation, 2017, 11(12): 1877–1885.
    [37] Krizhevsky A, Sutskever I, and Hinton G E. ImageNet classification with deep convolutional neural networks[C]. Proceedings of Advances in Neural Information Processing Systems, Lake Tahoe, Nevada, USA, 2012, 1: 1097–1105
    [38] Tao C, Pan H B, Li Y S, et al. Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(12): 2438–2442. DOI: 10.1109/LGRS.2015.2482520
    [39] Hinton G E, Osindero S, and Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527–1554. DOI: 10.1162/neco.2006.18.7.1527
    [40] 袁秋壮, 魏松杰, 罗娜. 基于深度学习神经网络的SAR星上目标识别系统研究[J]. 上海航天, 2017, 34(5): 46–53. DOI: 10.19328/j.cnki.1006-1630.2017.05.007

    Yuan Qiu-zhuang, Wei Song-jie, and Luo Na. Research on SAR satellite target recognition system based on deep learning neural network[J]. Aerospace Shanghai, 2017, 34(5): 46–53. DOI: 10.19328/j.cnki.1006-1630.2017.05.007
    [41] Wang C, Zhang H, Wu F, et al.. Ship classification with deep learning using COSMO-SkyMed SAR data[C]. Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA, 2017: 558–561
    [42] Ding J, Chen B, Liu H W, et al. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364–368.
    [43] 朱同宇. 基于深度学习的合成孔径雷达地面目标识别技术研究[D]. [硕士论文], 哈尔滨工业大学, 2017

    Zhu Tong-yu. Research on ground target recognition techniques of synthetic aperture radar based on deep learning[D]. [Master dissertation], Harbin Institute of Technology, 2017
    [44] Tang X X, Zhang X L, Shi J, et al.. SAR deception jamming target recognition based on the shadow feature[C]. Proceedings of the 25th European Signal Processing Conference, Kos, Greece, 2017: 2491–2495
    [45] Scarpa G, Gargiulo M, Mazza A, et al. A CNN-based fusion method for feature extraction from sentinel data[J]. Remote Sensing, 2018, 10(2): 236. DOI: 10.3390/rs10020236
    [46] Hughes L H, Schmitt M, Mou L C, et al. Identifying corresponding patches in SAR and optical images with a Pseudo-Siamese CNN[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 784–788. DOI: 10.1109/LGRS.2018.2799232
    [47] Bentes C, Velotto D, and Tings B. Ship classification in TerraSAR-X images with convolutional neural networks[J]. IEEE Journal of Oceanic Engineering, 2018, 43(1): 258–266. DOI: 10.1109/JOE.2017.2767106
    [48] Wang L, Scott K A, Xu L L, et al. Sea ice concentration estimation during melt from dual-pol SAR scenes using deep convolutional neural networks: A case study[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4524–4533. DOI: 10.1109/TGRS.2016.2543660
    [49] Zhou Y, Wang H P, Xu F, et al. Polarimetric SAR image classification using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(12): 1935–1939. DOI: 10.1109/LGRS.2016.2618840
    [50] 徐丰, 王海鹏, 金亚秋. 深度学习在SAR目标识别与地物分类中的应用[J]. 雷达学报, 2017, 6(2): 136–148. DOI: 10.12000/JR16130

    Xu Feng, Wang Hai-peng, and Jin Ya-qiu. Deep learning as applied in SAR target recognition and Terrain classification[J]. Journal of Radars, 2017, 6(2): 136–148. DOI: 10.12000/JR16130
    [51] Zhang Z M, Wang H P, Xu F, et al. Complex-valued convolutional neural network and its application in polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12): 7177–7188. DOI: 10.1109/TGRS.2017.2743222
    [52] Hu W M, Hu R G, Xie N H, et al. Image classification using multiscale information fusion based on saliency driven nonlinear diffusion filtering[J]. IEEE Transactions on Image Processing, 2014, 23(4): 1513–1526. DOI: 10.1109/TIP.2014.2303639
    [53] 赵娟萍, 郭炜炜, 柳彬, 等. 基于概率转移卷积神经网络的含噪标记SAR图像分类[J]. 雷达学报, 2017, 6(5): 514–523. DOI: 10.12000/JR16140

    Zhao Juan-ping, Guo Wei-wei, Liu Bin, et al. Convolutional neural network-based SAR image classification with noisy labels[J]. Journal of Radars, 2017, 6(5): 514–523. DOI: 10.12000/JR16140
    [54] Wang P Y, Zhang H, and Patel V M. SAR image despeckling using a convolutional neural network[J]. IEEE Signal Processing Letters, 2017, 24(12): 1763–1767. DOI: 10.1109/LSP.2017.2758203
    [55] Chierchia G, Cozzolino D, Poggi G, et al.. SAR image despeckling through convolutional neural networks[C]. Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA, 2017: 5438–5441
    [56] Bai Y B, Gao C, Singh S, et al. A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(1): 43–47. DOI: 10.1109/LGRS.2017.2772349
    [57] Iandola F N, Han S, Moskewicz M W, et al.. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size[EB/OL]. http://arxiv.org/abs/1602.07360.2016
    [58] Zagoruyko S and Komodakis N. Wide residual networks[EB/OL]. http://arxiv.org/abs/1605.07146.2017
    [59] 于文倩. 基于自适应频域信息和深度学习的SAR图像分割[D]. [硕士论文], 西安电子科技大学, 2014

    Yu Wen-qian. SAR image segmentation based on the adaptive frequency domain information and deep learning[D]. [Master dissertation], Xidian University, 2014
    [60] 高蓉. 面向极化SAR地物分类的稀疏深度网络[D]. [硕士论文], 西安电子科技大学, 2015

    Gao Rong. Sparse deep networks for polarimetric SAR Terrain classification[D]. [Master dissertation], Xidian University, 2015
    [61] Hou B, Kou H D, and Jiao L C. Classification of polarimetric SAR images using multilayer autoencoders and superpixels[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(7): 3072–3081. DOI: 10.1109/JSTARS.2016.2553104
    [62] 石俊飞, 刘芳, 林耀海, 等. 基于深度学习和层次语义模型的极化SAR分类[J]. 自动化学报, 2017, 43(2): 215–226. DOI: 10.16383/j.aas.2017.c150660

    Shi Jun-fei, Liu Fang, Lin Yao-hai, et al. Polarimetric SAR image classification based on deep learning and hierarchical semantic model[J]. Acta Automatica Sinica, 2017, 43(2): 215–226. DOI: 10.16383/j.aas.2017.c150660
    [63] 康妙, 计科峰, 冷祥光, 等. 基于栈式自编码器特征融合的SAR图像车辆目标识别[J]. 雷达学报, 2017, 6(2): 167–176. DOI: 10.12000/JR16112

    Kang Miao, Ji Kengfeng, Leng Xiangguang, et al. SAR target recognition with feature fusion based on stacked autoencoder[J]. Journal of Radars, 2017, 6(2): 167–176. DOI: 10.12000/JR16112
    [64] Chen G D, Li Y, Sun G M, et al. Application of deep networks to oil spill detection using polarimetric synthetic aperture radar images[J]. Applied Sciences, 2017, 7(10): 968. DOI: 10.3390/app7100968
    [65] 涂松. 高分辨率SAR图像目标快速提取算法研究[D]. [博士论文], 国防科学技术大学, 2016

    Tu Song. Fast and accurate target extraction for high-resolution SAR imagery[D]. [Ph.D. dissertation], National University of Defense Technology, 2016
    [66] Kang M, Ji K F, Leng X G, et al. Synthetic aperture radar target recognition with feature fusion based on a stacked autoencoder[J]. Sensors, 2017, 17(1): 192.
    [67] De S, Pirrone D, Bovolo F, et al.. A novel change detection framework based on deep learning for the analysis of multi-temporal polarimetric SAR images[C]. Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA, 2017: 5193–5196
    [68] 阮怀玉. 基于稀疏表示和深度学习的SAR图像目标识别研究[D]. [硕士论文], 中国科学技术大学, 2016

    Ruan Huai-yu. SAR image target recognition based on sparse representation and deep learning[D]. [Master dissertation], University of Science and Technology of China, 2016
    [69] 罗小欢. 基于深度置信网的极化SAR图像分类[D]. [硕士论文], 西安电子科技大学, 2014

    Luo Xiao-huan. Classification of polarimetric SAR images based on deep belief networks[D]. [Master dissertation], Xidian University, 2014
    [70] Lv Q, Dou Y, Niu X, et al.. Classification of land cover based on deep belief networks using polarimetric RADARSAT-2 data[C]. Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 2014: 4679–4682
    [71] 赵昌锋. 基于深度学习的干涉SAR图像分类[D]. [硕士论文], 西安电子科技大学, 2015

    Zhao Chang-feng. InSAR image classification based on deep learning[D]. [Master dissertation], Xidian University, 2015
    [72] Quan D, Wang S, Ning M D, et al.. Using deep neural networks for synthetic aperture radar image registration[C]. Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 2016: 2799–2802
    [73] Lundén J and Koivunen V. Deep learning for HRRP-based target recognition in multistatic radar systems[C]. Proceedings of 2016 IEEE Radar Conference, Philadelphia, PA, USA, 2016: 1–6
    [74] 张欢. 基于射频隐身的机载雷达系统软件实现及HRRP目标识别研究[D]. [硕士论文], 南京航空航天大学, 2016

    Zhang Huan. RF stealth based airborne radar system simulation and HRRP target recognition research[D]. [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2016
    [75] Zhao F X, Liu Y X, Huo K, et al. Radar HRRP target recognition based on stacked autoencoder and extreme learning machine[J]. Sensors, 2018, 18(1): 173.
    [76] Feng B, Chen B, and Liu H W. Radar HRRP target recognition with deep networks[J]. Pattern Recognition, 2017, 61: 379–393. DOI: 10.1016/j.patcog.2016.08.012
    [77] Xia J Y, Li X, and Liu Y X. Application of a new restricted Boltzmann machine to radar target recognition[C]. Proceedings of 2016 Progress in Electromagnetic Research Symposium, Shanghai, China, 2016: 2195–2201
    [78] Pan M, Jiang J, Kong Q P, et al. Radar HRRP target recognition based on t-SNE segmentation and discriminant deep belief network[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1609–1613. DOI: 10.1109/LGRS.2017.2726098
    [79] Jithesh V, Sagayaraj M J, and Srinivasa K G. LSTM recurrent neural networks for high resolution range profile based radar target classification[C]. Proceedings of the 2017 3rd International Conference on Computational Intelligence & Communication Technology, Ghaziabad, India, 2017: 1–6
    [80] Shao Y M, Guo S, Sun L, et al.. Human motion classification based on range information with deep convolutional neural network[C]. Proceedings of the 2017 4th International Conference on Information Science and Control Engineering, Changsha, China, 2017: 1519–1523
    [81] Kim Y and Toomajian B. Hand gesture recognition using micro-Doppler signatures with convolutional neural network[J]. IEEE Access, 2016, 4: 7125–7130. DOI: 10.1109/ACCESS.2016.2617282
    [82] Kim Y and Moon T. Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(1): 8–12. DOI: 10.1109/LGRS.2015.2491329
    [83] Trommel R P, Harmanny R I A, Cifola L, et al.. Multi-target human gait classification using deep convolutional neural networks on micro-Doppler spectrograms[C]. Proceedings of 2016 European Radar Conference, London, UK, 2016: 81–84
    [84] Park J, Javier R J, Moon T, et al. Micro-Doppler based classification of human aquatic activities via transfer learning of convolutional neural networks[J]. Sensors, 2016, 16(12): 1990. DOI: 10.3390/s16121990
    [85] Kim Y and Li Y. Human Activity classification with transmission and reflection coefficients of on-body antennas through deep convolutional neural networks[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(5): 2764–2768. DOI: 10.1109/TAP.2017.2677918
    [86] Jokanovic B, Amin M, and Ahmad F. Radar fall motion detection using deep learning[C]. Proceedings of 2016 IEEE Radar Conference, Philadelphia, PA, USA, 2016: 1–6
    [87] Seyfioğlu M S, Gürbüz S Z, Özbayoğlu A M, et al.. Deep learning of micro-Doppler features for aided and unaided gait recognition[C]. Proceedings of 2017 IEEE Radar Conference, Seattle, WA, USA, 2017: 1125–1130
    [88] 张国祥. 基于深度神经网络的人车分类算法[D]. [硕士论文], 西安电子科技大学, 2016

    Zhang Guo-xiang. Vehicle-pedestrian classification based on deep neural networks[D]. [Master dissertation], Xidian University, 2016
    [89] Seyfioğlu M S and Gürbüz S Z. Deep neural network initialization methods for micro-Doppler classification with low training sample support[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(12): 2462–2466. DOI: 10.1109/LGRS.2017.2771405
    [90] Wang S W, Song J, Lien J, et al.. Interacting with soli: Exploring fine-grained dynamic gesture recognition in the radio-frequency spectrum[C]. Proceedings of the 29th Annual Symposium on User Interface Software and Technology, Tokyo, Japan, 2016: 851–860
    [91] Jokanović B and Amin M. Fall detection using deep learning in range-Doppler radars[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(1): 180–189. DOI: 10.1109/TAES.2017.2740098
    [92] 李腾飞, 秦永彬. 基于迭代深度学习的缺陷检测[J]. 计算机与数字工程, 2017, 45(6): 1133–1137. DOI: 10.3969/j.issn.1672-9722.2017.06.025

    Li Teng-fei and Qin Yong-bin. Feature detection base on iterative deep learning[J]. Computer and Digital Engineering, 2017, 45(6): 1133–1137. DOI: 10.3969/j.issn.1672-9722.2017.06.025
    [93] 郑晓飞, 郭创, 姚斌, 等. 基于深度学习的航空传感器故障诊断方法[J]. 计算机工程, 2017, 43(7): 281–287. DOI: 10.3969/j.issn.1000-3428.2017.07.047

    Zheng Xiao-fei, Guo Chuang, Yao Bin, et al. Fault diagnosis method for aerial sensor based on deep learning[J]. Computer Engineering, 2017, 43(7): 281–287. DOI: 10.3969/j.issn.1000-3428.2017.07.047
    [94] 孙志军, 薛磊, 许阳明. 基于深度学习的边际Fisher分析特征提取算法[J]. 电子与信息学报, 2013, 35(4): 805–811. DOI: 10.3724/SP.J.1146.2012.00949

    Sun Zhi-jun, Xue Lei, and Xu Yang-ming. Marginal fisher feature extraction algorithm based on deep learning[J]. Journal of Electronics&Information Technology, 2013, 35(4): 805–811. DOI: 10.3724/SP.J.1146.2012.00949
    [95] Ashiquzzaman A, Tushar A K, Islam M R, et al.. Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network[M]. Kim K J, Kim H, and Baek N. IT Convergence and Security. Singapore: Springer, 2018: 35–43
    [96] Feng X Y, Liang Y C, Shi X H, et al. Overfitting reduction of text classification based on AdaBELM[J]. Entropy, 2017, 19(7): 330. DOI: 10.3390/e19070330
    [97] Yu Z, Tan E L, Ni D, et al. A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 22(3): 874–885. DOI: 10.1109/JBHI.2017.2705031
    [98] 熊红凯, 高星, 李邵辉, 等. 可解释化、结构化、多模态化的深度神经网络[J]. 模式识别与人工智能, 2018, 31(1): 1–11. DOI: 10.16451/j.cnki.issn1003-6059.201801001

    Xiong Hong-kai, Gao Xing, Li Shao-hui, et al. Interpretable structured multi-modal deep neural network[J]. Pattern Recognition and Artificial Intelligence, 2018, 31(1): 1–11. DOI: 10.16451/j.cnki.issn1003-6059.201801001
    [99] Zeiler M D, Krishnan D, Taylor G W, et al.. Deconvolutional networks[C]. Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010: 2528–2535
    [100] Zeiler M D, Taylor G W, and Fergus R. Adaptive deconvolutional networks for mid and high level feature learning[C]. Proceedings of 2011 International Conference on Computer Vision, Barcelona, Spain, 2011: 2018–2025
    [101] Dosovitskiy A and Brox T. Inverting visual representations with convolutional networks[C]. Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016: 4829–4837
    [102] Zuallaert J, Kim M, Saeys Y, et al.. Interpretable convolutional neural networks for effective translation initiation site prediction[C]. Proceedings of 2017 IEEE International Conference on Bioinformatics and Biomedicine, Kansas City, MO, USA, 2017: 1233–1237
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  • 收稿日期:  2018-05-22
  • 修回日期:  2018-07-18
  • 网络出版日期:  2018-08-28

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