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Detection and Classification of Maritime Target with Micro-motion Based on CNNs
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摘要: 该文利用深度学习的高维特征泛化学习能力，将卷积神经网络(CNN)用于海上目标微多普勒的检测和分类。首先，在海面微动目标模型的基础上，在实测海杂波背景中分别构建4种类型微动信号的2维时频图，并作为训练和测试数据集；然后，分别采用LeNet, AlexNet和GoogLeNet 3种CNN模型进行二元检测和多种微动类型分类，并进行比较，研究信杂比对检测和分类性能的影响。最后，与传统的支持向量机方法进行比较，结果表明，所提方法能够智能学习微动特征，具有更好的检测和分类性能，可为杂波背景下的雷达动目标检测和识别提供新的技术途径。Abstract: In this paper, Convolutional Neural Networks (CNN) are used to detect and classify micro-Doppler effects of maritime targets by using generalized learning ability for high-dimensional features. Based on the micro-motion model of maritime targets, two-dimensional time-frequency maps of four types of micro-motion signals are constructed in the measured sea clutter background. These maps were used as training and test datasets. Furthermore, three types of CNN models, i.e., LeNet, AlexNet, and GoogleNet, are used in binary detection and multiple micro-motion classifications. The effects of signal-to-noise ratio on detection and classification performance are also studied. Compared with the traditional support vector machine method, the proposed method can learn the micro-motion features intelligently, and has performed better in detection and classification. Thus, this study can provide a new technical approach for radar target detection and recognition under a cluttered background.
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图 2 CNN中各卷积层的数据特征(以LeNet为例)
Figure 2. Data characteristics of each convolution layer in CNN (e.g., LeNet)
图 6 不同信杂比下目标检测分类性能
Figure 6. Performances of target classification under different SCRs
表 1 微动参数设置
Table 1. Configuration of micro-motion parameters
运动类型 初速度
(m/s)加速度
(m/s2)急动度
(m/s3)采样点数 运动类型 角速度 微动周期(s) 采样点数 匀加速 [5, 15] [–16, 16] – 211 Hz, 0.50 s 微动Ⅰ [16] $\left| {{{\bar \omega }_{x{\rm m}}}} \right|$=[0.34, 0.42] rad/s
$\left| {{{\bar \omega }_{y{\rm m}}}} \right|$=[0.15, 0.17] rad/s
$\left| {{{\bar \omega }_{z{\rm m}}}} \right|$=[0.07, 0.09] rad/s${T_x}$=26.4
${T_y}$=11.2
${T_z}$=33.028 Hz, 8 s 非匀变速 [50, 300] [–160, 160] [–160, 160] 214 Hz, 0.25 s 微动Ⅱ [16] $\left| {{{\bar \omega }_{x{\rm m}}}} \right|$=[0.61, 0.65] rad/s
$\left| {{{\bar \omega }_{y{\rm m}}}} \right|$=[0.95, 1.07] rad/s
$\left| {{{\bar \omega }_{z{\rm m}}}} \right|$=[0.52, 0.56] rad/s${T_x}$=12.2
${T_y}$=6.7
${T_z}$=14.2210 Hz, 8 s 
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表 2 CNN模型训练时长
Table 2. Training time of CNN models
模型 目标检测模型 目标分类模型 LeNet AlexNet GoogLeNet LeNet AlexNet GoogLeNet 训练用时(min) 68.00 37.52 175.00 27.92 38.40 54.43
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表 3 不同模型目标检测结果
Table 3. Results of target detection of different models
模型 LeNet AlexNet GoogLeNet 虚警概率 1.24% 0.04% 0.24% 检测概率 92.28% 84.44% 90.94% 仿真时间(min) 41.92 57.15 43.77
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表 4 基于CNN和SVM的海面目标检测与分类结果比较
Table 4. Comparison of maritime targets detection and classification results based on CNN and SVM
分类方法 虚警概率 检测概率(%) 识别概率(SCR=–4 dB)(%) SCR=4 dB SCR=–4 dB SCR=–12 dB SCR=–20 dB 匀变速 变加速 微动Ⅰ 微动Ⅱ CNN 0.0290 95.80 96.30 94.11 90.62 100.00 100.00 99.65 100.00 SVM 0.2207 80.52 79.90 77.81 77.31 84.30 84.46 73.56 94.25
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