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| Citation: | Su Ningyuan, Chen Xiaolong, Guan Jian, Mou Xiaoqian, Liu Ningbo. 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
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Detection and Classification of Maritime Target with Micro-motion Based on CNNs
DOI: 10.12000/JR18077 CSTR: 32380.14.JR18077
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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. -
References
[1] Darzikolaei M A, Ebrahimzade A, and Gholami E. Classification of radar clutters with artificial neural network[C]. Proceedings of the 2nd International Conference on Knowledge-Based Engineering and Innovation, Tehran, Iran, 2015: 577–581.[2] 陈小龙, 关键, 何友. 微多普勒理论在海面目标检测中的应用及展望[J]. 雷达学报, 2013, 2(1): 123–134. DOI: 10.3724/SP.J.1300.2013.20102Chen Xiao-long, Guan Jian, and He You. Applications and prospect of micro-motion theory in the detection of sea surface target[J].Journal of Radars, 2013, 2(1): 123–134. DOI: 10.3724/SP.J.1300.2013.20102[3] 罗迎, 张群, 王国正, 等. 基于复图像OMP分解的宽带雷达微动特征提取方法[J]. 雷达学报, 2012, 1(4): 361–369. DOI: 10.3724/SP.J.1300.2012.20065Luo Ying, Zhang Qun, Wang Guo-zheng, et al. Micro-motion signature extraction method for wideband radar based on complex image OMP decomposition[J]. Journal of Radars, 2012, 1(4): 361–369. DOI: 10.3724/SP.J.1300.2012.20065[4] Chen X L, Guan J, Bao Z H, et al. Detection and extraction of target with micromotion in spiky sea clutter via short-time fractional Fourier transform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1002–1018. DOI: 10.1109/TGRS.2013.2246574[5] Chen X L, Guan J, Li X Y, et al. Effective coherent integration method for marine target with micromotion via phase differentiation and radon-Lv’s distribution[J]. IET Radar,Sonar&Navigation, 2015, 9(9): 1284–1295. DOI: 10.1049/iet-rsn.2015.0100[6] Wagner S A. SAR ATR by a combination of convolutional neural network and support vector machines[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6): 2861–2872. DOI: 10.1109/TAES.2016.160061[7] 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/JR16037Tian Zhuang-zhuang, Zhan Rong-hui, Hu Jie-min, et al. SAR ATR based on convolutional neural network[J]. Journal of Radars, 2016, 5(3): 320–325. DOI: 10.12000/JR16037[8] 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[9] 王俊, 郑彤, 雷鹏, 等. 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/JR18040Wang Jun, Zheng Tong, Lei Peng, et al. Survey of study on deep learning in radar[J]. Journal of Radars, 2018, 7(4): 395–411. DOI: 10.12000/JR18040[10] 徐彬, 陈渤, 刘宏伟, 等. 基于注意循环神经网络模型的雷达高分辨率距离像目标识别[J]. 电子与信息学报, 2016, 38(12): 2988–2995. DOI: 10.11999/JEIT161034Xu Bin, Chen Bo, Liu Hong-wei, et al. Attention-based recurrent neural network model for radar high-resolution range profile target recognition[J]. Journal of Electronics&Information Technology, 2016, 38(12): 2988–2995. DOI: 10.11999/JEIT161034[11] 王星, 周一鹏, 周冬青, 等. 基于深度置信网络和双谱对角切片的低截获概率雷达信号识别[J]. 电子与信息学报, 2016, 38(11): 2972–2976. DOI: 10.11999/JEIT160031Wang Xing, Zhou Yi-peng, Zhou Dong-qing, et al. Research on low probability of intercept radar signal recognition using deep belief network and bispectra diagonal slice[J]. Journal of Electronics&Information Technology, 2016, 38(11): 2972–2976. DOI: 10.11999/JEIT160031[12] 徐真, 王宇, 李宁, 等. 一种基于CNN的SAR图像变化检测方法[J]. 雷达学报, 2017, 6(5): 483–491. DOI: 10.12000/JR17075Xu Zhen, Wang Robert, Li Ning, et al. A novel approach to change detection in SAR images with CNN classification[J]. Journal of Radars, 2017, 6(5): 483–491. DOI: 10.12000/JR17075[13] 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/JR16130Xu 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[14] 陈小龙, 董云龙, 李秀友, 等. 海面刚体目标微动特征建模及特性分析[J]. 雷达学报, 2015, 4(6): 630–638. DOI: 10.12000/JR15079Chen Xiao-long, Dong Yun-long, Li Xiu-you, et al. Modeling of micromotion and analysis of properties of rigid marine targets[J]. Journal of Radars, 2015, 4(6): 630–638. DOI: 10.12000/JR15079[15] Prusa J D and Khoshgoftaar T M. Improving deep neural network design with new text data representations[J]. Journal of Big Data, 2017, 4: 7. DOI: 10.1186/s40537-017-0065-8[16] 高建军. 多径和海杂波干扰下的舰船ISAR成像及横向定标[D]. [博士论文], 哈尔滨工业大学, 2010.Gao Jian-jun. ISAR ship imaging and cross-range scaling with multipath and sea clutter interference[D]. [Ph.D. dissertation], Harbin Institute of Technology, 2010. Relative Articles
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LIU Yuzhou, CAI Tianyi, LI Yachao, et al. A range and azimuth combined two-dimensional NCS algorithm for spaceborne-missile bistatic forward-looking SAR[J]. Journal of Radars, 2023, 12(6): 1202–1214. doi: 10.12000/JR23144
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- Figure 1. Structure of three CNNs used in this paper
- Figure 2. Data characteristics of each convolution layer in CNN (e.g., LeNet)
- Figure 3. Processing flow diagram of the proposed method
- Figure 4. Examples of datasets
- Figure 5. Examples of wrong judgment
- Figure 7. Results of target classification via different models
- Figure 6. Performances of target classification under different SCRs