[1]阮怀林,杨兴宇.基于栈式稀疏自编码器的有源欺骗干扰识别[J].探测与控制学报,2018,40(04):62.[doi:.]
 RUAN Huailin,YANG Xingyu.Radar Active Deception Identification Method Based on Stacked Sparse Autoencoder[J].,2018,40(04):62.[doi:.]
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基于栈式稀疏自编码器的有源欺骗干扰识别()
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《探测与控制学报》[ISSN:1008-1194/CN:61-1316/TJ]

卷:
40
期数:
2018年04期
页码:
62
栏目:
出版日期:
2018-08-26

文章信息/Info

Title:
Radar Active Deception Identification Method Based on Stacked Sparse Autoencoder
文章编号:
1008-1194(2018)04-0062-06
作者:
阮怀林杨兴宇
国防科技大学电子对抗学院,安徽 合肥 230037
Author(s):
RUAN HuailinYANG Xingyu
Electronic Countermeasure Institute of National University of Defense Technology,Hefei 230037, China
关键词:
欺骗干扰干扰识别时频分析深度学习栈式稀疏自编码器
Keywords:
deception jamming jamming recognition time-frequency distribution deep learning stacked sparse autoencoder
分类号:
TN974
DOI:
.
文献标志码:
A
摘要:
针对传统方法在欺骗干扰特征提取时需要依赖人工经验的缺点,提出了基于栈式稀疏自编码器(Stacked Sparse Autoencoder)的有源欺骗干扰识别算法。该算法对干扰下的雷达接收信号进行时频分析,对时频特征进行降维,利用无标签样本对特征提取模型进行预训练,再通过少量有标签样本进行监督精校。最后利用soft max分类器完成有源干扰的识别。仿真实验证明,该方法有较高的识别率,特别是该方法受信噪比影响较少,说明了深度学习方法应用于雷达欺骗干扰信号分类识别领域的可行性。相较于其他文献方法,该算法拥有更好的实验效果,证明了该方法的优越性。
Abstract:
Aimed at the deficiency of traditional technique of radar active deception feature extraction which heavily rely on artificial experience, a recognition algorithm was proposed based on stacked sparse auto-encoder. In this method, spwvd distribution of received radar signal under jamming was given, and dimensionality reduction was implemented with a series of image processing methods. In the phase of pre-training, stacked sparse auto-encoder model was trained with unlabeled samples by greedy layer-wise training. On this basis, network parameters were fine-tuned with label information. Finally, the soft max classifier was used to recognize the active jamming. The simulation results showed that this method had high recognition rate, especially the influence of SNR on this method was less and the feasibility of applying the deep learning method to the classification and recognition of radar deception jamming signal. Compared with other literature methods, the algorithm had better experimental results, and the superiority of the method was proved.

参考文献/References:

[1]杨少奇, 田波, 李欣, 等. 基于时频图像特征提取的LFM雷达有源欺骗干扰识别[J]. 空军工程大学, 2016, 17(1): 56-59.
[2]李建勋, 唐斌, 吕强. 双谱特征提取在欺骗式干扰方式识别中的应用[J]. 电子科技大学学报, 2009,38(3): 329-332.
[3]田晓, 唐斌. 基于归一化小波分解能量比的雷达有源欺骗干扰识别[J]. 数据采集与处理, 2013, 28(04): 416-420.
[4]熊英, 刘旻, 唐斌, 等. 基于盒维数与L-Z复杂度的雷达复合干扰特征提取方法[J]. 数据采集与处理, 2008, 23(6): 663-667.
[5]杨少奇, 田波. 频谱弥散和切片组合欺骗式干扰的识别算法[J]. 探测与控制学报, 2016, 38(6): 62-67.
[6]李芳, 熊英, 唐斌. 基于霍夫曼树和逆云模型的雷达拖引干扰识别[J]. 数据采集与处理, 2013, 28(4): 497-501.
[7]康妙, 计科峰, 冷祥光, 等. 基于栈式自编码器特征融合的SAR图像车辆目标识别[J]. 雷达学报, 2017, 6(2): 167-176.
[8]HINTON G, SALAKHUTDINOV R. Reducing the dimensionality of data with neural network[J]. Science, 2006, 313(5786): 504-507.
[9]周志文, 黄高明, 高俊, 等. 一种深度学习的雷达辐射源识别算法[J]. 西安电子科技大学学报(自然科学版), 2017, 44(3): 85-90.
[10]JüRGEN S. Deep learning in networks: an overview[J]. NeuralNetworks, 2015, 61: 85-117.
[11]熊坤来, 罗景青, 吴世龙. 基于时频图像和神经网络的LPI雷达信号调制识别[J]. 弹箭与制导学报, 2011, 31(05): 230-233.
[12]Gonzalez R C, Woods R E. Digital image processing[M].Englewood Cliffs, New Jersey: Prebtice-Hall INC, 2002.
[13]Jose Dolz, Nacim Betrouni, Betrouni, el al. Stacking denosing auto-encoder in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study[J]. Computerized Medical Imaging and Fraphics, 2016, 52: 8-18.
[14]DING C, TAO D. Robust face recognition via multimodal deep face representation[J]. IEEE Transactions on Multimedia, 2015, 17(11): 2049-2058.
[15]李艳莉, 田晓. 基于积谱矩阵局部二值模式的欺骗干扰识别[J]. 电讯技术, 2015(04): 441-446.

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备注/Memo

备注/Memo:
收稿日期:2017-12-28
作者简介:阮怀林(1964—),男,安徽合肥人,博士生导师,教授,研究方向:空间信号处理。E-mail: 350761904@qq.com
更新日期/Last Update: 2018-09-14