高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

卷积神经网络在雷达自动目标识别中的研究进展

贺丰收 何友 刘准钆 徐从安

贺丰收, 何友, 刘准钆, 徐从安. 卷积神经网络在雷达自动目标识别中的研究进展[J]. 电子与信息学报, 2020, 42(1): 119-131. doi: 10.11999/JEIT180899
引用本文: 贺丰收, 何友, 刘准钆, 徐从安. 卷积神经网络在雷达自动目标识别中的研究进展[J]. 电子与信息学报, 2020, 42(1): 119-131. doi: 10.11999/JEIT180899
Fengshou HE, You HE, Zhunga LIU, Cong’an XU. Research and Development on Applications of Convolutional Neural Networks of Radar Automatic Target Recognition[J]. Journal of Electronics and Information Technology, 2020, 42(1): 119-131. doi: 10.11999/JEIT180899
Citation: Fengshou HE, You HE, Zhunga LIU, Cong’an XU. Research and Development on Applications of Convolutional Neural Networks of Radar Automatic Target Recognition[J]. Journal of Electronics and Information Technology, 2020, 42(1): 119-131. doi: 10.11999/JEIT180899

卷积神经网络在雷达自动目标识别中的研究进展

doi: 10.11999/JEIT180899
基金项目: 国家自然科学基金(61672431, 61790550, 91538201)
详细信息
    作者简介:

    贺丰收:男,1979年生,高级工程师,博士生,研究方向为雷达数据处理,多源信息融合,深度神经网络等

    何友:男,1956年生,中国工程院院士,博士生导师,研究方向为多源信息融合,信号检测,雷达数据处理等

    刘准钆:男,1984年生,教授,研究方向为多源信息融合,证据推理,模式识别

    徐从安:男,1987年生,博士,讲师,研究方向为多目标跟踪,信息融合等

    通讯作者:

    贺丰收 hefengshou1979@163.com

  • 中图分类号: TN953

Research and Development on Applications of Convolutional Neural Networks of Radar Automatic Target Recognition

Funds: The National Natural Science Foundation of China (61672431, 61790550, 91538201)
  • 摘要: 自动目标识别(ATR)是雷达信息处理领域的重要研究方向。由于卷积神经网络(CNN)无需进行特征工程,图像分类性能优越,因此在雷达自动目标识别领域研究中受到越来越多的关注。该文综合论述了CNN在雷达图像处理中的应用进展。首先介绍了雷达自动目标识别相关知识,包括雷达图像的特性,并指出了传统的雷达自动目标识别方法局限性。给出了CNN卷积神经网络原理、组成和在计算机视觉领域的发展历程。然后着重介绍了CNN在雷达自动目标识别中的研究现状,其中详细介绍了合成孔径雷达(SAR)图像目标的检测与识别方法。接下来对雷达自动目标识别面临的挑战进行了深入分析。最后对CNN新理论、新模型,以及雷达新成像技术和未来复杂环境下的应用进行了展望。
  • 图  1  LeNet-5网络的结构示意图

    图  2  ILSVRC历年的冠军成绩

    图  3  深度网络和深度卷积网络在雷达图像领域发表的文章数示意图

    表  1  光学图像和雷达图像的差异

    特性光学图像雷达图像
    波段可见光,红外微波段
    信号形式多波段灰度信息单波段复信号
    成像原理能量聚焦积累相位相干积累
    尺度特性和成像距离有关目标尺寸不随成像距离变化
    成像方向俯仰角-方位角距离向-方位角
    下载: 导出CSV

    表  2  部分典型网络的参数总结

    LeNet5AlexNetOverfeatfastVGG16GoogleNetV1ResNet50
    输入图像尺寸28×28227×227231×231224×224224×224224×224
    卷积层数量255135753
    全连接层数量233311
    卷积核大小53,5,113,5,1131,3,5,71,3,7
    步长11,41,411,21,2
    权值参数数量60 k61 M146 M138 M7 M25.5 M
    乘积运算数量341 k724 M2.8 G15.5 G1.43 G3.9 G
    Top-5误差16.414.27.46.75.25
    下载: 导出CSV

    表  3  MSTAR数据集的目标类型和样本数量

    数据集2S1BMP2BRD M2BTR 60BTR 70D7T62T72ZIL 131ZSU 234
    训练集299233298256233299299298299299
    测试集274587274195196274196274274274
    下载: 导出CSV

    表  4  常见数据增强技术

    名称主要方法
    旋转变换将图像旋转一定角度
    翻转变换沿水平或垂直方向翻转图像
    缩放变换放大或缩小图像
    平移变换在图像平面上对图像进行平移
    尺度变换对图像按照置顶的尺度因子进行缩放,改变图像内容的大小或模糊程度
    反射变换对称变换,包括轴反射变换和镜面反射变换
    噪声扰动在图像内增加噪声,如指数噪声,高斯噪声,瑞利噪声,椒盐噪声等
    下载: 导出CSV

    表  5  基于CNN的目标检测方法对比

    方法提出场合核心思想MAP(%)主要特点
    候选窗方法RCNNECCV 2014选择搜索方法生成候选窗66.0训练分多个阶段,每个候选窗都需要用CNN处理,占用磁盘空间大,处理效率低
    Fast RCNNICCV2015加入了SPPnet70.0选择搜索方法生成候选窗,耗时长,无法满足实时应用
    Faster RCNNNIPS2015提出了RPN网络,融合区域生成与CNN73.2性能与速度较好的折中,但区域生成方式计算量依然很大,不能实时处理
    R-FCNNIPS2016RPN+位置敏感的预测层+ROI polling+投票决策层76.6速度比Faster RCNN快,且精度相当
    回归方法YOLOCVPR2016将检测问题变为回归问题57.9没有区域生成步骤,网格回归的定位性能较弱,检测精度不高。
    SSDECCV2016YOLO+Proposal+多尺度73.9速度非常快,性能也不错
    下载: 导出CSV

    表  6  CNN在雷达图像识别应用进展的思想与方法概要

    提升类型主要思想引用文献和方法概要说明
    快速算法快速寻优预训练文献[47]:带动量小批量随机梯度下降,快速寻找全局最优点
    文献[45]:预训练较浅卷积网络,实现无监督快速检测。
    文献[53]:用大样本数据对卷积网络进行预训练
    用其他结构取代全连接层文献[40,47]:低自由度稀疏连通卷积结构
    文献[39]:SVM代替FC
    文献[53]:用超限学习机替换FC
    抽取特征再训练文献[54]:先抽取特征再训练的两步快速训练方法
    提升算法提高网络的泛化能力文献[47]:Dropout和早期停止
    文献[52]:将卷积层与2维PCA方法结合
    代价函数改进文献[46]:代价函数中引入类别可分性度量提高类别区分能力
    含噪样本训练文献[49]:基于概率转移模型增强含噪标记下分类模型鲁棒性。
    扩展算法迁移学习文献[26,53,55]:大样本预训练,迁移学习加快训练速度
    CAD模型仿真文献[56]: 采用CAD模型目标仿真解决SAR真实数据有限问题
    文献[57]: CAD模型生成不同方位和俯仰角度的HRRP图像
    预处理提升信息的利用率文献[41]:形态学成分分析预处理提升性能
    文献[58]:采用去噪自编码器预训练
    小样本深度训练网络文献[42,44]:卷积高速公路单元在小样本条件下训练深度网络
    文献[59]:无监督和有监督训练结合,应对标签数据有限情况
    下载: 导出CSV
  • [1] KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. Imagenet classification with deep convolutional neural networks[C]. The 25th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2012: 1097–1105.
    [2] CHENG Gong, HAN Junwei, and LU Xiaoqiang. Remote sensing image scene classification: benchmark and state of the art[J]. Proceedings of the IEEE, 2017, 105(10): 1865–1883. doi:  10.1109/JPROC.2017.2675998
    [3] 陈小龙, 关键, 何友, 等. 高分辨稀疏表示及其在雷达动目标检测中的应用[J]. 雷达学报, 2017, 6(3): 239–251. doi:  10.12000/JR16110

    CHEN Xiaolong, GUAN Jian, HE You, et al. High-resolution sparse representation and its applications in radar moving target detection[J]. Journal of Radars, 2017, 6(3): 239–251. doi:  10.12000/JR16110
    [4] BALL J E, ANDERSON D T, and CHAN C S. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community[J]. Journal of Applied Remote Sensing, 2017, 11(4): 042609. doi:  10.1117/1.JRS.11.042609
    [5] PEI Jifang, HUANG Yulin, HUO Weibo, et al. SAR automatic target recognition based on multiview deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2196–2210. doi:  10.1109/tgrs.2017.2776357
    [6] GOODFELLOW I, BENGIO Y, and COURVILLE A. Deep Learning[M]. Cambridge, Massachusetts: MIT Press, 2016.
    [7] LECUN Yann, BOTTOU Léon, BENGIO Yoshua, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278–2324. doi:  10.1109/5.726791
    [8] RUSSAKOVSKY O, DENG Jia, SU Hao, et al. Imagenet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211–252. doi:  10.1007/s11263-015-0816-y
    [9] ZEILER M D and FERGUS R. Visualizing and understanding convolutional networks[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 818–833.
    [10] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1–9.
    [11] SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. http://arxiv.org/abs/1409.1556, 2014.
    [12] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
    [13] HU Jie, SHEN Li, ALBANIE S, et al. Squeeze-and-excitation networks[EB/OL]. https://arxiv.org/abs/1709.01507, 2017.
    [14] 许强, 李伟, LOUMBI P. 深度卷积神经网络在SAR自动目标识别领域的应用综述[J]. 电讯技术, 2018, 58(1): 106–112. doi:  10.3969/j.issn.1001-893x.2018.01.019

    XU Qiang, LI Wei, and LOUMBI P. Applications of Deep convolutional neural network in SAR automatic target recognition: a summarization[J]. Telecommunication Engineering, 2018, 58(1): 106–112. doi:  10.3969/j.issn.1001-893x.2018.01.019
    [15] 苏宁远, 陈小龙, 关键, 等. 基于卷积神经网络的海上微动目标检测与分类方法[J]. 雷达学报, 2018, 7(5): 565–574. doi:  10.12000/JR18077

    SU Ningyuan, CHEN Xiaolong, GUAN Jian, et al. Detection and classification of maritime target with micro-motion based on CNNs[J]. Journal of Radars, 2018, 7(5): 565–574. doi:  10.12000/JR18077
    [16] 杜兰, 刘彬, 王燕, 等. 基于卷积神经网络的SAR图像目标检测算法[J]. 电子与信息学报, 2016, 38(12): 3018–3025. doi:  10.11999/JEIT161032

    DU Lan, LIU Bin, WANG Yan, et al. Target detection method based on convolutional neural network for SAR image[J]. Journal of Electronics &Information Technology, 2016, 38(12): 3018–3025. doi:  10.11999/JEIT161032
    [17] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.
    [18] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 346–361.
    [19] GIRSHICK R. Fast R-CNN[C]. The IEEE international Conference on Computer Vision, Santiago, Chile, 2015: 1440–1448.
    [20] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]. The 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 91–99.
    [21] DAI Jifeng, LI Yi, HE Kaiming, et al. R-FCN: Object detection via region-based fully convolutional networks[C]. The 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 379–387.
    [22] KONG Tao, YAO Anbang, CHEN Yurong, et al. Hypernet: Towards accurate region proposal generation and joint object detection[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 845–853.
    [23] LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 936–944.
    [24] HE Kaiming, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]. The 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2980–2988.
    [25] WANG Sifei, CUI Zongyong, and CAO Zongjie. Target recognition in large scene SAR images based on region proposal regression[C]. The 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, USA, 2017: 3297–3300.
    [26] LI Jianwei, QU Changwen, and SHAO Jiaqi. Ship detection in SAR images based on an improved faster R-CNN[C]. The 2017 SAR in Big Data Era: Models, Methods and Applications, Beijing, China, 2017: 1–6.
    [27] KANG Miao, LENG Xiangguang, LIN Zhao, et al. A modified faster R-CNN based on CFAR algorithm for SAR ship detection[C]. The 2017 International Workshop on Remote Sensing with Intelligent Processing, Shanghai, China, 2017: 1–4.
    [28] KANG Miao, JI Kefeng, LENG Xiangguang, et al. Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection[J]. Remote Sensing, 2017, 9(8): 860. doi:  10.3390/rs9080860
    [29] JIAO Jiao, ZHANG Yue, SUN Hao, et al. A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection[J]. IEEE Access, 2018, 6: 20881–20896. doi:  10.1109/ACCESS.2018.2825376
    [30] ZHONG Yanfei, HAN Xiaobing, and ZHANG Liangpei. Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 138: 281–294. doi:  10.1016/j.isprsjprs.2018.02.014
    [31] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]. The 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779–788.
    [32] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot multibox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37.
    [33] WANG Yuanyuan, WANG Chao, ZHANG Hong, et al. Combing single shot multibox detector with transfer learning for ship detection using Chinese Gaofen-3 images[C]. The 2017 Progress in Electromagnetics Research Symposium - Fall, Singapore, 2018: 712–716.
    [34] WANG Yuanyuan, WANG Chao, and ZHANG Hong. Combining a single shot multibox detector with transfer learning for ship detection using sentinel-1 SAR images[J]. Remote Sensing Letters, 2018, 9(8): 780–788. doi:  10.1080/2150704X.2018.1475770
    [35] KONG Tao, SUN Fuchun, YAO Anbang, et al. Ron: Reverse connection with objectness prior networks for object detection[C]. The 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 5244–5252.
    [36] CUI Zongyong, DANG Sihang, CAO Zongjie, et al. SAR target recognition in large scene images via region-based convolutional neural networks[J]. Remote Sensing, 2018, 10(5): 776. doi:  10.3390/rs10050776
    [37] NI Jiacheng and XU Yuelei. SAR automatic target recognition based on a visual cortical system[C]. The 6th International Congress on Image and Signal Processing, Hangzhou, China, 2013: 778–782.
    [38] CHEN Sizhe and WANG Haipeng. SAR target recognition based on deep learning[C]. The 2014 International Conference on Data Science and Advanced Analytics, Shanghai, China, 2014: 541–547.
    [39] WAGNER S. Combination of convolutional feature extraction and support vector machines for radar ATR[C]. The 17th International Conference on Information Fusion, Salamanca, Spain, 2014: 1–6.
    [40] WANG Haipeng, CHEN Sizhe, XU Feng, et al. Application of deep-learning algorithms to MSTAR data[C]. The 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 2015: 3743–3745.
    [41] WAGNER S. Morphological component analysis in SAR images to improve the generalization of ATR systems[C]. The 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing, Pisa, Italy, 2015: 46–50.
    [42] SCHWEGMANN C P, KLEYNHANS W, SALMON B P, et al. Very deep learning for ship discrimination in Synthetic Aperture Radar imagery[C]. The 2016 IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 2016: 104–107.
    [43] CHO J H and PARK C G. Additional feature CNN based automatic target recognition in SAR image[C]. The 40th Asian Conference on Defence Technology, Tokyo, Japan, 2017: 1–4.
    [44] LIN Zhao, JI Kefeng, KANG Miao, et al. Deep convolutional highway unit network for SAR target classification with limited labeled training data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(7): 1091–1095. doi:  10.1109/lgrs.2017.2698213
    [45] HE Hao, WANG Shicheng, YANG Dongfang, et al. SAR target recognition and unsupervised detection based on convolutional neural network[C]. The 2017 Chinese Automation Congress, Jinan, China, 2017: 435–438.
    [46] 田壮壮, 占荣辉, 胡杰民, 等. 基于卷积神经网络的SAR图像目标识别研究[J]. 雷达学报, 2016, 5(3): 320–325. doi:  10.12000/JR16037

    TIAN Zhuangzhuang, ZHAN Ronghui, HU Jiemin, et al. SAR ATR based on convolutional neural network[J]. Journal of Radars, 2016, 5(3): 320–325. doi:  10.12000/JR16037
    [47] CHEN Sizhe, WANG Haipeng, XU Feng, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi:  10.1109/tgrs.2016.2551720
    [48] WILMANSKI M, KREUCHER C, and LAUER J. Modern approaches in deep learning for SAR ATR[J]. SPIE, 2016, 9843: 98430N. doi:  10.1117/12.2220290
    [49] 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 Juanping, GUO Weiwei, 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
    [50] AMRANI M and JIANG Feng. Deep feature extraction and combination for synthetic aperture radar target classification[J]. Journal of Applied Remote Sensing, 2017, 11(4): 042616. doi:  10.1117/1.Jrs.11.042616
    [51] WANG Ning, WANG Yinghua, LIU Hongwei, et al. Feature-fused SAR target discrimination using multiple convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(10): 1695–1699. doi:  10.1109/lgrs.2017.2729159
    [52] ZHENG Ce, JIANG Xue, and LIU Xingzhao. Generalized synthetic aperture radar automatic target recognition by convolutional neural network with joint use of two-dimensional principal component analysis and support vector machine[J]. Journal of Applied Remote Sensing, 2017, 11(4): 046007. doi:  10.1117/1.Jrs.11.046007
    [53] 刘晨, 曲长文, 周强, 等. 基于卷积神经网络迁移学习的SAR图像目标分类[J]. 现代雷达, 2018, 40(3): 38–42. doi:  10.16592/j.cnki.1004-7859.2018.03.009

    LIU Chen, QU Changwen, ZHOU Qiang, et al. SAR image target classification based on convolutional neural network transfer learning[J]. Modern Radar, 2018, 40(3): 38–42. doi:  10.16592/j.cnki.1004-7859.2018.03.009
    [54] LI Xuan, LI Chunsheng, WANG Pengbo, et al. SAR ATR based on dividing CNN into CAE and SNN[C]. The 5th IEEE Asia-Pacific Conference on Synthetic Aperture Radar, Singapore, 2015: 676–679.
    [55] 李松, 魏中浩, 张冰尘, 等. 深度卷积神经网络在迁移学习模式下的SAR目标识别[J]. 中国科学院大学学报, 2018, 35(1): 75–83. doi:  10.7523/j.issn.2095-6134.2018.01.010

    LI Song, WEI Zhonghao, ZHANG Bingchen, et al. Target recognition using the transfer learning-based deep convolutional neural networks for SAR images[J]. Journal of University of Chinese Academy of Sciences, 2018, 35(1): 75–83. doi:  10.7523/j.issn.2095-6134.2018.01.010
    [56] ØDEGAARD N, KNAPSKOG A O, COCHIN C, et al. Classification of ships using real and simulated data in a convolutional neural network[C]. The 2016 IEEE Radar Conference, Philadelphia, USA, 2016: 1–6.
    [57] KARABAYIR O, YUCEDAG O M, KARTAL M Z, et al. Convolutional neural networks-based ship target recognition using high resolution range profiles[C]. The 18th International Radar Symposium, Prague, Czech Republic, 2017.
    [58] BENTES C, VELOTTO D, and LEHNER S. Target classification in oceanographic SAR images with deep neural networks: Architecture and initial results[C]. The 2015 IEEE International Geoscience and Remote Sensing Symposium, Milan, Italy, 2015: 3703–3706.
    [59] WANG Zhaocheng, DU Lan, WANG Fei, et al. Multi-scale target detection in SAR image based on visual attention model[C]. The 5th IEEE Asia-Pacific Conference on Synthetic Aperture Radar, Singapore, 2015: 704–709.
    [60] YUAN Lele. A time-frequency feature fusion algorithm based on neural network for HRRP[J]. Progress in Electromagnetics Research, 2017, 55: 63–71. doi:  10.2528/PIERM16123002
    [61] BENGIO Y, MESNARD T, FISCHER A, et al. STDP-compatible approximation of backpropagation in an energy-based model[J]. Neural Computation, 2017, 29(3): 555–577. doi:  10.1162/NECO_a_00934
    [62] LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi:  10.1038/nature14539
    [63] HOWARD A G, ZHU Menglong, CHEN Bo, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[EB/OL]. http://arxiv.org/abs/1704.04861, 2017.
    [64] HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]. The IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2261–2269.
    [65] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]. The 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014: 2672–2680.
  • [1] 缪祥华, 单小撤.  基于密集连接卷积神经网络的入侵检测技术研究, 电子与信息学报. doi: 10.11999/JEIT190655
    [2] 董小伟, 韩悦, 张正, 曲洪斌, 高国飞, 陈明钿, 李博.  基于多尺度加权特征融合网络的地铁行人目标检测算法, 电子与信息学报. doi: 10.11999/JEIT200450
    [3] 杨钧智, 吴金亮, 智军.  基于多尺度圆周频率滤波与卷积神经网络的遥感图像飞机目标检测方法研究, 电子与信息学报. doi: 10.11999/JEIT200144
    [4] 杜兰, 魏迪, 李璐, 郭昱辰.  基于半监督学习的SAR目标检测网络, 电子与信息学报. doi: 10.11999/JEIT190783
    [5] 邹鲲.  认知雷达的未知目标检测, 电子与信息学报. doi: 10.11999/JEIT170254
    [6] 谌德荣, 王文斌, 刘丙太, 姜威, 俞达, 宫久路.  旋转不变梯度直方图目标描述方法, 电子与信息学报. doi: 10.11999/JEIT150546
    [7] 杜兰, 刘彬, 王燕, 刘宏伟, 代慧.  基于卷积神经网络的SAR图像目标检测算法, 电子与信息学报. doi: 10.11999/JEIT161032
    [8] 于洪波, 王国宏, 曹倩, 王娜.  一种高脉冲重复频率雷达微弱目标检测跟踪方法, 电子与信息学报. doi: 10.11999/JEIT140924
    [9] 方明, 戴奉周, 刘宏伟, 王小谟, 秦童.  基于联合稀疏恢复的宽带雷达动目标检测方法, 电子与信息学报. doi: 10.11999/JEIT150442
    [10] 郑作虎, 王首勇.  基于Alpha稳定分布杂波模型的雷达目标检测方法, 电子与信息学报. doi: 10.3724/SP.J.1146.2014.00072
    [11] 杨宇翔, 同武勤, 熊瑾煜.  一种无源雷达高速机动目标检测新方法, 电子与信息学报. doi: 10.3724/SP.J.1146.2013.01984
    [12] 刘博, 常文革.  步进调频宽带雷达距离扩展目标频域检测算法, 电子与信息学报. doi: 10.3724/SP.J.1146.2012.01309
    [13] 王建, 袁宵, 李禹, 黄春琳, 粟毅.  利用互相关和Hough变换快速检测探地雷达目标, 电子与信息学报. doi: 10.3724/SP.J.1146.2012.01134
    [14] 战立晓, 汤子跃, 朱振波.  一种米波相控阵雷达四代机目标检测算法, 电子与信息学报. doi: 10.3724/SP.J.1146.2012.01249
    [15] 吴仁彪, 毛建, 王晓亮, 贾琼琼.  航管一次雷达抗风电场干扰目标检测方法, 电子与信息学报. doi: 10.3724/SP.J.1146.2012.00923
    [16] 武昕, 王岩飞, 刘畅.  基于压缩感知理论的随机噪声雷达目标检测算法研究, 电子与信息学报. doi: 10.3724/SP.J.1146.2011.01067
    [17] 吴兆平, 符渭波, 苏涛, 郑纪彬.  基于快速Radon-Fourier变换的雷达高速目标检测, 电子与信息学报. doi: 10.3724/SP.J.1146.2011.01180
    [18] 杨勇, 冯德军, 王雪松, 张文明, 肖顺平.  低空雷达导引头海面目标检测性能分析, 电子与信息学报. doi: 10.3724/SP.J.1146.2010.01407
    [19] 基于目标CSAR回波模型的SAR自动目标识别算法, 电子与信息学报. doi: 10.3724/SP.J.1146.2010.00192
    [20] 肖慧, 胡卫东, 郁文贤.  基于二次混频DPT的LFMCW雷达多目标检测和参数估计, 电子与信息学报. doi: 10.3724/SP.J.1146.2007.00946
  • 加载中
  • 图(3) / 表ll (6)
    计量
    • 文章访问数:  2899
    • HTML全文浏览量:  1520
    • PDF下载量:  303
    • 被引次数: 0
    出版历程
    • 收稿日期:  2018-09-18
    • 修回日期:  2019-02-18
    • 网络出版日期:  2019-03-21
    • 刊出日期:  2020-01-21

    目录

      /

      返回文章
      返回

      官方微信,欢迎关注