Research and Development on Applications of Convolutional Neural Networks of Radar Automatic Target Recognition
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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。
Abstract:Automatic Target Recognition(ATR) is an important research area in the field of radar information processing. Because the deep Convolution Neural Network(CNN) does not need to carry out feature engineering and the performance of image classification is superior, it attracts more and more attention in the field of radar automatic target recognition. The application of CNN to radar image processing is reviewed in this paper. Firstly, the related knowledges including the characteristics of the radar image is introduced, and the limitations of traditional radar automatic target recognition methods are pointed out. The principle, composition, development of CNN and the field of computer vision are introduced. Then, the research status of CNN in radar automatic target recognition is provided. The detection and recognition method of SAR image are presented in detail. The challenge of radar automatic target recognition is analyzed. Finally, the new theory and model of convolution neural network, the new imaging technology of radar and the application to complex environments in the future are prospected.
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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 
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表 2 部分典型网络的参数总结
LeNet5 AlexNet Overfeatfast VGG16 GoogleNetV1 ResNet50 输入图像尺寸 28×28 227×227 231×231 224×224 224×224 224×224 卷积层数量 2 5 5 13 57 53 全连接层数量 2 3 3 3 1 1 卷积核大小 5 3,5,11 3,5,11 3 1,3,5,7 1,3,7 步长 1 1,4 1,4 1 1,2 1,2 权值参数数量 60 k 61 M 146 M 138 M 7 M 25.5 M 乘积运算数量 341 k 724 M 2.8 G 15.5 G 1.43 G 3.9 G Top-5误差 – 16.4 14.2 7.4 6.7 5.25
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表 3 MSTAR数据集的目标类型和样本数量
数据集 2S1 BMP2 BRD M2 BTR 60 BTR 70 D7 T62 T72 ZIL 131 ZSU 234 训练集 299 233 298 256 233 299 299 298 299 299 测试集 274 587 274 195 196 274 196 274 274 274
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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
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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 RCNN ECCV 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 RCNN ICCV2015 加入了SPPnet 70.0 选择搜索方法生成候选窗，耗时长，无法满足实时应用 Faster RCNN NIPS2015 提出了RPN网络，融合区域生成与CNN 73.2 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-FCN NIPS2016 RPN+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 YOLO CVPR2016 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。 SSD ECCV2016 YOLO+Proposal+多尺度 73.9 速度非常快，性能也不错
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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
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