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基于深度置信網(wǎng)絡(luò)和雙譜對角切片的低截獲概率雷達(dá)信號識別

王星,  周一鵬,  周東青,  陳忠輝,  田元榮

王星, 周一鵬, 周東青, 陳忠輝, 田元榮. 基于深度置信網(wǎng)絡(luò)和雙譜對角切片的低截獲概率雷達(dá)信號識別[J]. 電子與信息學(xué)報, 2016, 38(11): 2972-2976. doi: 10.11999/JEIT160031
引用本文: 王星, 周一鵬, 周東青, 陳忠輝, 田元榮. 基于深度置信網(wǎng)絡(luò)和雙譜對角切片的低截獲概率雷達(dá)信號識別[J]. 電子與信息學(xué)報, 2016, 38(11): 2972-2976. doi: 10.11999/JEIT160031
WANG Xing, ZHOU Yipeng, ZHOU Dongqing, CHEN Zhonghui, TIAN Yuanrong. 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
Citation: WANG Xing, ZHOU Yipeng, ZHOU Dongqing, CHEN Zhonghui, TIAN Yuanrong. 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

基于深度置信網(wǎng)絡(luò)和雙譜對角切片的低截獲概率雷達(dá)信號識別

doi: 10.11999/JEIT160031
  • 1.

    (空軍工程大學(xué)航空航天工程學(xué)院 西安 710038) ②(解放軍95357部隊(duì) 佛山 528227)

基金項(xiàng)目: 

國家自然科學(xué)基金(61372167)，航空科學(xué)基金(20152096019)

Research on Low Probability of Intercept Radar Signal Recognition Using Deep Belief Network and Bispectra Diagonal Slice

  • 1.

    (Institute of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi&rsquo

  • 2.

    (Unit 95357 of PLA, Foshan 528227, China)

Funds: 

The National Natural Science Foundation of China (61372167), The Aeronautical Science Foundation of China (20152096019)

  • 摘要: 基于深度置信網(wǎng)絡(luò)(DBN)對信號雙譜對角切片(BDS)結(jié)構(gòu)特征進(jìn)行學(xué)習(xí)，實(shí)現(xiàn)低截獲概率(LPI)雷達(dá)信號識別。該方法首先建立基于受限玻爾茲曼機(jī)(RBM)的DBN模型，對LPI雷達(dá)信號的BDS數(shù)據(jù)進(jìn)行逐層無監(jiān)督貪心學(xué)習(xí)，然后運(yùn)用后向傳播(BP)機(jī)制在有監(jiān)督學(xué)習(xí)方式下根據(jù)學(xué)習(xí)誤差對DBN模型參數(shù)進(jìn)行微調(diào)，最后基于該BDS-DBN模型實(shí)現(xiàn)未知信號的分類和識別。理論分析和仿真結(jié)果表明，信噪比高于8 dB時，基于BDS和DBN的識別方法對調(diào)頻連續(xù)波(FMCW), Frank, Costas, FSK/PSK 4類LPI信號的綜合識別率保持在93.4%以上，高于傳統(tǒng)的主成分分析加支持向量機(jī)法(PCA-SVM)和主成分分析加線性判別分析法(PCA-LDA)。
    Abstract: A novel recognition algorithm for Low Probability of Intercept (LPI) radar signal based on deep learning of radar signals Bispectra Diagonal Slice (BDS) is proposed in this paper. Firstly, a Deep Belief Network (DBN) model is established on stacked Restricted Boltzmann Machines (RBM), then the model is used for layer-by-layer unsupervised greedy learning of radar signals BDS. Secondly, a Back Propagation (BP) algorithm is applied to fine tune parameters of DBN model with a supervised way according to learning error. Finally, the BDS-DBN model is constructed to classify and recognize unknown LPI signals. The theoretical analysis and the simulation results show that, the average recognition accuracy of the proposed algorithm for Frequency Modulation Continuous Wave (FMCW), Frank, Costas and FSK/PSK signals can reach 93.4% or ever higher while the SNR is better than 8 dB, which is better than that of Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm and Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) algorithm.
    • HTML全文
  • PHILLIP E P. Detecting and Classing Low Probability of Intercept Radar (Second Edition)[M]. Norwood, MA, USA, Artech House, 2009: 1-15.
    LIU Y J, XIAO P, WU H C, et al. LPI radar signal detection based on radial integration of Choi-Williams time-frequency image[J]. Journal of Systems Engineering and Electronics, 2015, 26(5): 973-981. doi: 10.1109/JSEE.2015.00106.
    李娜, 王珂, 李保珠. 低截獲概率雷達(dá)信號檢測方法的優(yōu)化及應(yīng)用[J]. 光學(xué)精密工程, 2014, 22(11): 3122-3128. doi: 10. 3788/OPE. 20142211.3122.
    LI Na, WANG Ke, and LI Baozhu. Optimization and application of LPI radar signal detection method[J]. Optics and Precision Engineering, 2014, 22(11): 3122-3128. doi: 10.3788/OPE. 20142211.3122.
    蔡忠偉, 李建東. 基于雙譜的通信輻射源個體識別[J]. 通信學(xué)報, 2006, 28(2): 75-79. doi: 10.3321/j.issn:1000-436x.2007.02. 012.
    CAI Zhongwei and LI Jiandong. Study of transmitter individual identification based on bispectra[J]. Journal on Communications, 2006, 28(2): 75-79. doi: 10.3321/j.issn: 1000-436x.2007.02.012.
    王世強(qiáng), 張登福, 畢篤彥, 等. 雙譜二次特征在雷達(dá)信號識別中的應(yīng)用[J]. 西安電子科技大學(xué)學(xué)報(自然科學(xué)版), 2012, 39(2): 127-132. doi: 10.3969/j.issn.1001-2400.2012.02.021.
    WANG Shiqiang, ZHANG Dengfu, BI Duyan, et al. Research on recognizing the radar signal using the bispectrum cascade feature[J]. Journal of Xidian University, 2012, 39(2): 127-132. doi: 10.3969/j.issn.1001-2400.2012.02.021.
    徐書華, 黃本雄, 徐麗娜. 基于SIB/PCA的通信輻射源個體識別[J]. 華中科技大學(xué)學(xué)報(自然科學(xué)版), 2008, 36(7): 14-17. doi: 10.3321/j.issn:1671-4512.2008.07.004.
    XU Shuhua, HUANG Benxiong, and XU Lina. Identification of individual radio transmitters using SIB/PCA[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2008, 36(7): 14-17. doi: 10.3321/j.issn:1671- 4512.2008.07.004.
    胡振, 傅昆, 張長水. 基于深度學(xué)習(xí)的作曲家分類問題[J] . 計算機(jī)研究與發(fā)展, 2014, 51(9): 1945-1954. doi: 10.7544/issn. 1000-1239.2014.20140189.
    HU Zhen, FU Kun, and ZHANG Changshui. Audio classical composer identification by deep neural network[J]. Journal of Computer Research and Development, 2014, 51(9): 1945-1954. doi: 10.7544/issn.1000-1239.2014.20140189.
    SCHMIDHUBER J. Deep learning in neural networks: An overview[J]. Neural Networks, 2014, 61: 85-117. doi: 10.1016/ j.neunet.2014.09.003.
    尹寶才, 王文通, 王立春. 深度學(xué)習(xí)研究綜述[J]. 北京工業(yè)大學(xué)學(xué)報, 2015, 41(1): 48-59. doi: 10.11936/bjutxb2014100026.
    YIN Baocai, WANG Wentong, and WANG Lichun. Review of deep learning[J]. Journal of Beijing University of Technology, 2015, 41(1): 48-59. doi: 10.11936/bjutxb2014100026.
    HINTON G E, OSINDERO S, and TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.doi: 10.1162/neco.2006.18.7.1527.
    SARIKAYA R, HINTON G E, and DEORAS A. Application of deep belief networks for natural language understanding[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2014, 22(4): 778-784. doi: 10.1109/TASLP. 2014.2303296.
    HINTON G, LI D, DONG Y, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups[J]. IEEE Signal Processing Magazine, 2012, 29(6): 82-97. doi: 10.1109/MSP.2012.2205597.
    TABOADA and FERNANDO L. Detection and classification of low probability of intercept radar signals using parallel filter arrays and higher order statistics[D]. [Ph.D. dissertation], Naval Postgraduate School, 2002.
    張旭. 基于信號分析的無線設(shè)備指紋特征提取[D]. [碩士論文], 北京郵電大學(xué), 2014: 13-14.
    ZHANG Xu. Wireless devices fingerprint feature extraction based on signal analysis[D]. [Master dissertation], Beijing University of Posts and Telecommunications, 2014: 13-14.
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出版歷程
  • 收稿日期:  2016-01-16
  • 修回日期:  2016-07-14
  • 刊出日期:  2016-11-19

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