A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine

Zhao Feixiang; Liu Yongxiang; Huo Kai

doi:10.12000/JR18048

Volume 7 Issue 5

Nov. 2018

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > Journal of Radars > 2018 > 7(5): 613-621

XIA Deping, ZHANG Liang, WU Tao, et al. A multiple interference suppression algorithm based on airborne bistatic polarization radar[J]. Journal of Radars, 2022, 11(3): 399–407. doi: 10.12000/JR21212

Citation:

Zhao Feixiang, Liu Yongxiang, Huo Kai. A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine[J]. Journal of Radars, 2018, 7(5): 613-621. doi: 10.12000/JR18048

Citation:

PDF( 1204 KB)

A Radar Target Classification Algorithm Based on Dropout Constrained Deep Extreme Learning Machine

DOI: 10.12000/JR18048

College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Funds: The National Natural Science Foundation of China (61422114, 61501481), The Natural Science Fund for Distinguished Young Scholars of Hunan Province (2015JJ1003)

Received Date: 2018-06-22
Rev Recd Date: 2018-08-29
Publish Date: 2018-10-28

Abstract

Abstract

Radar target classification is very important in military and civilian fields. Extreme Learning Machines (ELMs) are widely used in classification because of their fast learning speed and good generalization performance. However, because of their shallow architecture, ELMs may not effectively capture the data high level abstractions. Although many researchers have proposed the Deep Extreme Learning Machine (DELM), which can be used to automatically learn high level feature representations, the model easily falls into overfitting when the training sample is limited. To address this issue, Dropout Constrained Deep Extreme Learning Machine (DCDELM) is proposed in this paper. The experimental results on the measured radar data show that the accuracy of the proposed algorithm can reach 93.37%, which is 5.25% higher than that of the stacked autoencoder algorithm, and 8.16% higher than that of the traditional DELM algorithm.
- Extreme Learning Machine (ELM),
- Deep learning,
- Dropout constrained,
- Radar target classification,
- Stacked autoencoder

FullText(HTML)

References(27)

References

[1]	Liu Y X, Zhu D K, Li X, et al. Micromotion characteristic acquisition based on wideband radar phase[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6): 3650–3657. DOI: 10.1109/TGRS.2013.2274478
[2]	Bigdeli B and Pahlavani P. Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine-based data fusion system[J]. Journal of Applied Remote Sensing, 2017, 11(1): 016007. DOI: 10.1117/1.JRS.11.016007
[3]	Shi J F, Li L L, Liu F, et al. Unsupervised polarimetric synthetic aperture radar image classification based on sketch map and adaptive Markov random field[J]. Journal of Applied Remote Sensing, 2016, 10(2): 025008. DOI: 10.1117/1.JRS.10.025008
[4]	Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504–507. DOI: 10.1126/science.1127647
[5]	Lecun Y, Bengio Y, and Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. DOI: 10.1038/nature14539
[6]	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. DOI: 10.3390/s18010173
[7]	Abdel-Hamid O, Mohamed A R, Jiang H, et al. Convolutional neural networks for speech recognition[J]. IEEE/ACM Transactions on Audio,Speech,and Language Processing, 2014, 22(10): 1533–1545. DOI: 10.1109/TASLP.2014.2339736
[8]	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. DOI: 10.1109/LGRS.2015.2513754
[9]	Wang X L, Guo R, and Kambhamettu C. Deeply-learned feature for age estimation[C]. Proceedings of 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2015: 534–541. DOI: 10.1109/WACV.2015.77.
[10]	Huang G B, Zhu Q Y, and Siew C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1/3): 489–501. DOI: 10.1016/j.neucom.2005.12.126
[11]	Huang G, Huang G B, Song S J, et al. Trends in extreme learning machines: A review[J]. Neural Networks, 2015, 61: 32–48. DOI: 10.1016/j.neunet.2014.10.001
[12]	Liang N Y, Huang G B, Saratchandran P, et al. A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE Transactions on Neural Networks, 2006, 17(6): 1411–1423. DOI: 10.1109/TNN.2006.880583
[13]	Zong W W and Huang G B. Face recognition based on extreme learning machine[J]. Neurocomputing, 2011, 74(16): 2541–2551. DOI: 10.1016/j.neucom.2010.12.041
[14]	Liu N and Wang H. Evolutionary extreme learning machine and its application to image analysis[J]. Journal of Signal Processing Systems, 2013, 73(1): 73–81. DOI: 10.1007/s11265-013-0730-x
[15]	Ding S F, Zhang N, Zhang J, et al. Unsupervised extreme learning machine with representational features[J]. International Journal of Machine Learning and Cybernetics, 2017, 8(2): 587–595. DOI: 10.1007/s13042-015-0351-8
[16]	Zhao F X, Liu Y X, Huo K, et al. Radar target classification using an evolutionary extreme learning machine based on improved quantum-behaved particle swarm optimization[J]. Mathematical Problems in Engineering, 2017: 7273061. DOI: 10.1155/2017/7273061
[17]	Kasun L L C, Zhou H M, Huang G B, et al. Representational learning with ELMs for big data[J]. IEEE Intelligent Systems, 2013, 28(6): 31–34.
[18]	Yu W C, Zhuang F Z, He Q, et al. Learning deep representations via extreme learning machines[J]. Neurocomputing, 2015, 149: 308–315. DOI: 10.1016/j.neucom.2014.03.077
[19]	Zhu W T, Miao J, Qing L Y, et al.. Hierarchical extreme learning machine for unsupervised representation learning[C]. Proceedings of 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 2015: 1–8. DOI: 10.1109/IJCNN.2015.7280669.
[20]	Tang J X, Deng C W, and Huang G B. Extreme learning machine for multilayer perceptron[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(4): 809–821. DOI: 10.1109/TNNLS.2015.2424995
[21]	Hinton G E, Srivastava N, Krizhevsky A, et al.. Improving neural networks by preventing co-adaptation of feature detectors[OL]. https://arxiv.org/abs/1207.0580.2012.07.
[22]	Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: A simple way to prevent neural networks from overfitting[J]. The Journal of Machine Learning Research, 2014, 15(1): 1929–1958.
[23]	Iosifidis A, Tefas A, and Pitas I. DropELM: Fast neural network regularization with dropout and dropconnect[J]. Neurocomputing, 2015, 162: 57–66. DOI: 10.1016/j.neucom.2015.04.006
[24]	Baldi P and Sadowski P. The dropout learning algorithm[J]. Artificial Intelligence, 2014, 201: 78–122.
[25]	Wager S, Wang S D, and Liang P. Dropout training as adaptive regularization[C]. Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, 2013: 351–359.
[26]	Yang W X, Jin L W, Tao D C, et al. DropSample: A new training method to enhance deep convolutional neural networks for large-scale unconstrained handwritten Chinese character recognition[J]. Pattern Recognition, 2016, 58: 190–203. DOI: 10.1016/j.patcog.2016.04.007
[27]	Baldi P and Sadowski P. Understanding dropout[C]. Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, 2013: 2814–2822.

Relative Articles

[1]	LAN Lan, ZHANG Xiang, XU Jingwei, LIAO Guisheng. Main-lobe Deceptive Jammers with Array Radars Using Space-time Multidimensional Coding[J]. Journal of Radars, 2025, 14(2): 439-455. doi: 10.12000/JR24229
[2]	LAN Xiaoyu, HU Jiyan, LIANG Mingshen, MA Shuang. Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming[J]. Journal of Radars, 2025, 14(2): 280-292. doi: 10.12000/JR24175
[3]	SU Hanning, PAN Jiameng, BAO Qinglong, GUO Fucheng, HU Weidong. Anti-interrupted Sampling Repeater Jamming Method in the Waveform Domain before Matched Filtering[J]. Journal of Radars, 2024, 13(1): 240-252. doi: 10.12000/JR23149
[4]	LI Zhongyu, GUI Liang, HAI Yu, WU Junjie, WANG Dangwei, WANG Anle, YANG Jianyu. Ultrahigh-resolution ISAR Micro-Doppler Suppression Methodology Based on Variational Mode Decomposition and Mode Optimization[J]. Journal of Radars, 2024, 13(4): 852-865. doi: 10.12000/JR24043
[5]	ZHAO Kaifa, SONG Hu, LIU Rong, WANG Xinhai. Distributed Radar Main-lobe Interference Suppression Method Via Joint Optimization of Array Configuration and Subarray Element Number[J]. Journal of Radars, 2024, 13(6): 1355-1369. doi: 10.12000/JR24192
[6]	CHEN Zirui, JI Yifei, LIU Xiwang, ZHANG Yongsheng, DONG Zhen, CHEN Alei, LIU Weijian. Transient Interference Suppression Algorithm Based on Time Frequency Sparse Prior for Skywave OTHR[J]. Journal of Radars, 2024, 13(6): 1157-1169. doi: 10.12000/JR24188
[7]	CHEN Yan, WANG Zhanling, PANG Chen, LI Yongzhen, WANG Zhuang. Radar Active Deception Jamming Recognition Method Based on the Time-varying Polarization-conversion Metasurface[J]. Journal of Radars, 2024, 13(4): 929-940. doi: 10.12000/JR24028
[8]	WANG Rongqing, XIE Jingyang, TIAN Biao, XU Shiyou, CHEN Zengping. Integrated Jamming Perception and Parameter Estimation Method for Anti-interrupted Sampling Repeater Jamming[J]. Journal of Radars, 2024, 13(6): 1337-1354. doi: 10.12000/JR24153
[9]	NIE Lin, WEI Shunjun, LI Jiahui, ZHANG Hao, SHI Jun, WANG Mou, CHEN Siyuan, ZHANG Xinyan. Active Blanket Jamming Suppression Method for Spaceborne SAR Images Based on Regional Feature Refinement Perceptual Learning[J]. Journal of Radars, 2024, 13(5): 985-1003. doi: 10.12000/JR24072
[10]	DU Siyu, LIU Zhixing, WU Yaojun, SHA Minghui, QUAN Yinghui. Dense-repeated Jamming Suppression Algorithm Based on the Support Vector Machine for Frequency Agility Radar[J]. Journal of Radars, 2023, 12(1): 173-185. doi: 10.12000/JR22065
[11]	HAN Zhaoyun, CEN Xi, CUI Jiahe, LI Yachao, ZHANG Peng. Self-supervised Learning Method for SAR Interference Suppression Based on Abnormal Texture Perception[J]. Journal of Radars, 2023, 12(1): 154-172. doi: 10.12000/JR22168
[12]	GAI Jiyu, JIANG Wei, ZHANG Kaixiang, LIANG Zhennan, CHEN Xinliang, LIU Quanhua. A Method for Interrupted-Sampling Repeater Jamming Identification and Suppression Based on Differential Features[J]. Journal of Radars, 2023, 12(1): 186-196. doi: 10.12000/JR22058
[13]	XIA Deping, ZHANG Liang, WU Tao, MENG Xiangdong. A Multiple Interference Suppression Algorithm Based on Airborne Bistatic Polarization Radar[J]. Journal of Radars, 2022, 11(3): 399-407. doi: 10.12000/JR21212
[14]	ZOU Kun, LAI Lei, LUO Yanbo, LI Wei. Suppression of Non-Gaussian Clutter from Subspace Interference[J]. Journal of Radars, 2020, 9(4): 715-722. doi: 10.12000/JR19050
[15]	LIU Pingyu, LYU Xiaode, LIU Zhongsheng, ZHANG Hanliang. Research on Co-channel Interference Suppression Method for Passive Radar Based on the Jiont Processing of Primary and Reference Channels[J]. Journal of Radars, 2020, 9(6): 974-986. doi: 10.12000/JR19047
[16]	HUANG Yan, ZHAO Bo, TAO Mingliang, CHEN Zhanye, HONG Wei. Review of Synthetic Aperture Radar Interference Suppression[J]. Journal of Radars, 2020, 9(1): 86-106. doi: 10.12000/JR19113
[17]	MENG Zhichao, LU Jingyue, ZHANG Lei. Forward-looking Multi-channel SAR Adaptive Identification to Suppress Deception Jamming[J]. Journal of Radars, 2019, 8(1): 82-89. doi: 10.12000/JR18081
[18]	Zhou Chunhui, Li Fei, Li Ning, Zheng Huifang, Wang Xiangyu. Modified Eigensubspace-based Approach for Radio-frequency Interference Suppression of SAR Image[J]. Journal of Radars, 2018, 7(2): 235-243. doi: 10.12000/JR17025
[19]	Li Yongzhen, Hu Wanqiu, Sun Dou, Li Zhongwei. Scheme for Polarization Detection and Suppression of TRAD[J]. Journal of Radars, 2016, 5(6): 666-672. doi: 10.12000/JR16115
[20]	Zhang Chi, Li Yue-li, Zhou Zhi-min. Wall Clutter Mitigation in Through-the-Wall Imaging Radar with Sparse Array Antenna Based on Independent Component Analysis[J]. Journal of Radars, 2014, 3(5): 524-532. doi: 10.3724/SP.J.1300.2014.14066

Supplements(0)

Cited By

Cited by

Periodical cited type(5)

1.	王沙飞，朱梦韬，李云杰，杨健，李岩. 对先进多功能雷达系统行为的识别、推理与预测：综述与展望. 信号处理. 2024(01): 17-55 .
2.	陈士超，魏靖彪，范俊，魏玺章，王泽朝，孙谦，刘明. 基于判别性无穷模糊受限玻尔兹曼机模型的HRRP序列识别. 兵工学报. 2024(S1): 43-50 .
3.	赵娟娟. 基于统计模型的电子信息产业企业成长性分析. 中国电子商情. 2024(04): 64-66 .
4.	苏海龙，水鹏朗. 采用双迭代寻优算法的舰船复HRRP估计. 西安电子科技大学学报. 2023(06): 105-119 .
5.	赵春雷，姚嘉嵘，李京效，戚张行，魏汇赞. 地基雷达空中目标识别方法研究综述. 空天预警研究学报. 2023(05): 313-320+334 .

Other cited types(10)

Proportional views

Proportional views

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Get Citation

PDF

XML

Article views(3528) PDF downloads(384)