一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機號碼
標(biāo)題
留言內(nèi)容
驗證碼

一種新的時分多址信號射頻特征及其在特定輻射源識別中的應(yīng)用

潘一葦,  彭華,  李天昀,  王文雅

潘一葦, 彭華, 李天昀, 王文雅. 一種新的時分多址信號射頻特征及其在特定輻射源識別中的應(yīng)用[J]. 電子與信息學(xué)報, 2019, 41(11): 2661-2668. doi: 10.11999/JEIT190163
引用本文: 潘一葦, 彭華, 李天昀, 王文雅. 一種新的時分多址信號射頻特征及其在特定輻射源識別中的應(yīng)用[J]. 電子與信息學(xué)報, 2019, 41(11): 2661-2668. doi: 10.11999/JEIT190163
Yiwei PAN, Hua PENG, Tianyun LI, Wenya WANG. A Novel Radiometric Signature of Time-Division Multiple Access Signals and Its Application to Specific Emitter Identification[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2661-2668. doi: 10.11999/JEIT190163
Citation: Yiwei PAN, Hua PENG, Tianyun LI, Wenya WANG. A Novel Radiometric Signature of Time-Division Multiple Access Signals and Its Application to Specific Emitter Identification[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2661-2668. doi: 10.11999/JEIT190163

一種新的時分多址信號射頻特征及其在特定輻射源識別中的應(yīng)用

doi: 10.11999/JEIT190163

  • 戰(zhàn)略支援部隊信息工程大學(xué)信息系統(tǒng)工程學(xué)院 鄭州 450001

基金項目: 國家自然科學(xué)基金(61401511, U1736107)
詳細(xì)信息
    作者簡介:

    潘一葦：男，1990年生，博士生，研究方向為通信信號處理、特定輻射源識別

    彭華：男，1973年生，教授，研究方向為通信信號處理、軟件無線電

    李天昀：男，1979年生，副教授，研究方向為通信信號處理、軟件無線電

    王文雅：女，1991年生，碩士生，研究方向為通信信號處理、可見光通信

    通訊作者:

    潘一葦　novakd@163.com

  • 中圖分類號: TN911.7

A Novel Radiometric Signature of Time-Division Multiple Access Signals and Its Application to Specific Emitter Identification

  • Institute of Information System Engineering, Information Engineering University, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (61401511, U1736107)
  • 摘要: 時分多址(TDMA)信號特定輻射源識別(SEI)的性能主要受限于突發(fā)數(shù)據(jù)的長度。為此，該文提出一種新的射頻特征，從載波相位上揭示了相鄰時隙的用戶是否相同，為相同用戶的數(shù)據(jù)累積提供了依據(jù)。該文首先分析了特征的產(chǎn)生機理，并給出了提取方法；根據(jù)特征的統(tǒng)計特性，推導(dǎo)了自適應(yīng)的判決門限，實現(xiàn)了相鄰時隙用戶身份的檢測；在此基礎(chǔ)上，設(shè)計了新的SEI處理流程，通過數(shù)據(jù)累積打破了每個時隙單獨識別的傳統(tǒng)思維。實驗結(jié)果表明：該特征對噪聲具備良好的魯棒性，能夠?qū)崿F(xiàn)相鄰時隙用戶身份的準(zhǔn)確檢測；與傳統(tǒng)做法相比，新的處理流程能夠有效改善TDMA信號SEI的性能。
    Abstract: For Time-Division Multiple Access (TDMA) signals, the performance of Specific Emitter Identification (SEI) is primarily limited by burst duration. To remedy this shortcoming, a novel radiometric signature is presented, which reveals whether the users of the adjacent time slots are the same from a perspective of carrier phase, thereby providing the basis for data accumulation of the same user. First, the feature mechanism is introduced, as well as the extraction method. Thereafter, user identity detection of the adjacent slots is implemented with an adaptive threshold, which is derived from the distribution of the signature. Finally, a new SEI processing procedure is designed with data accumulation, which breaks the routine of identifying only one slot at a time. Simulation results demonstrate that the proposed signature is resilient against the noise, and can accurately detect the user identity of the adjacent slots. Compared with the traditional processing procedure, the proposed one can effectively improve the SEI performance of TDMA signals.
    • HTML全文

  • 圖  1  TDMA突發(fā)數(shù)據(jù)結(jié)構(gòu)

    圖  2  直接變頻發(fā)射機的典型結(jié)構(gòu)

    圖  3  傳統(tǒng)的TDMA信號SEI處理流程

    圖  4  根據(jù)一段實際信號計算得到的升序向量

    圖  5  新的TDMA信號SEI處理流程

    圖  6  不同判決門限下的檢測正確率

    圖  7  不同信噪比下的漏檢概率和虛檢概率

    圖  8  不同信噪比下有/無數(shù)據(jù)累積的識別正確率

    圖  9  不同觀測時長對算法性能的影響

    表  1  不同信噪比下的檢測性能

    信噪比${{{E_S}} / {{N_0}}}$(dB)判決門限$\gamma $檢測概率${P_{\rmq7j3ldu95}}$(%)檢測正確率${P_{\rm{c}}}$(%)
    100.058299.672397.2490
    120.046799.696997.6732
    140.038699.707798.0256
    160.033699.710898.2951
    180.030499.736998.4334
    200.028599.743198.5184
    220.027399.809298.6035
    240.026699.821598.6404
    下載: 導(dǎo)出CSV
  • DANEV B, ZANETTI D, and CAPKUN S. On physical-layer identification of wireless devices[J]. ACM Computing Surveys, 2012, 45(1): Article No.6. doi: 10.1145/2379776.2379782
    SPEZIO A E. Electronic warfare systems[J]. IEEE Transactions on Microwave Theory and Techniques, 2002, 50(3): 633–644. doi: 10.1109/22.989948
    MERCHANT K, REVAY S, STANTCHEV G, et al. Deep learning for RF device fingerprinting in cognitive communication networks[J]. IEEE Journal of Selected Topics in Signal Processing, 2018, 12(1): 160–167. doi: 10.1109/JSTSP.2018.2796446
    DING Lida, WANG Shilian, WANG Fanggang, et al. Specific emitter identification via convolutional neural networks[J]. IEEE Communications Letters, 2018, 22(12): 2591–2594. doi: 10.1109/LCOMM.2018.2871465
    KIM K, SPOONER C M, AKBAR I, et al. Specific emitter identification for cognitive radio with application to IEEE 802.11[C]. IEEE Global Telecommunications, New Orleans, USA, 2008: 1–5.
    HAN Jie, ZHANG Tao, REN Dongfang, et al. Communication emitter identification based on distribution of bispectrum amplitude and phase[J]. IET Science, Measurement & Technology, 2017, 11(8): 1104–1112. doi: 10.1049/iet-smt.2017.0024
    BERTONCINI C, RUDD K, NOUSAIN B, et al. Wavelet fingerprinting of radio-frequency identification (RFID) tags[J]. IEEE Transactions on Industrial Electronics, 2012, 59(12): 4843–4850. doi: 10.1109/TIE.2011.2179276
    ZHANG Jingwen, WANG Fanggang, DOBRE O A, et al. Specific emitter identification via Hilbert-Huang transform in single-hop and relaying scenarios[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(6): 1192–1205. doi: 10.1109/TIFS.2016.2520908
    任東方, 張濤, 韓潔, 等. 基于ITD與紋理分析的特定輻射源識別方法[J]. 通信學(xué)報, 2017, 38(12): 160–168. doi: 10.11959/j.issn.1000-436x.2017299

    REN Dongfang, ZHANG Tao, HAN Jie, et al. Specific emitter identification based on ITD and texture analysis[J]. Journal on Communications, 2017, 38(12): 160–168. doi: 10.11959/j.issn.1000-436x.2017299
    SATIJA U, TRIVEDI N, BISWAL G, et al. Specific emitter identification based on variational mode decomposition and spectral features in single hop and relaying scenarios[J]. IEEE Transactions on Information Forensics and Security, 2019, 14(3): 581–591. doi: 10.1109/TIFS.2018.2855665
    BRIK V, BANERJEE S, GRUTESER M, et al. Wireless device identification with radiometric signatures[C]. The 14th ACM International Conference on Mobile Computing and Networking, New York, USA, 2008: 116–127.
    PENG Linning, HU Aiqun, ZHANG Junqing, et al. Design of a hybrid RF fingerprint extraction and device classification scheme[J]. IEEE Internet of Things Journal, 2019, 6(1): 349–360. doi: 10.1109/JIOT.2018.2838071
    LIU Mingwei and DOHERTY J F. Specific emitter identification using nonlinear device estimation[C]. Proceedings of IEEE Sarnoff Symposium, Princeton, USA, 2008: 1–5.
    POLAK A C and GOECKEL D L. Wireless device identification based on RF oscillator imperfections[J]. IEEE Transactions on Information Forensics and Security, 2015, 10(12): 2492–2501. doi: 10.1109/TIFS.2015.2464778
    ZHANG Yi. Wireless transmitter IQ balance and sideband suppression[EB/OL]. http://www.analog.com/media/en/technical-documentation/applicationnotes/AN-1100.pdf, 2018.
    潘一葦, 彭華, 李天昀, 等. 針對特定輻射源識別的高精度符號同步方法[J]. 通信學(xué)報, 2018, 39(8): 106–112. doi: 10.11959/j.issn.1000-436x.2018132

    PAN Yiwei, PENG Hua, LI Tianyun, et al. High-precision symbol timing algorithm for specific emitter identification[J]. Journal on Communications, 2018, 39(8): 106–112. doi: 10.11959/j.issn.1000-436x.2018132
    DE OLIVEIRA M C and BITMEAD R R. High-fidelity modulation parameter estimation of non-cooperative transmitters: Carrier frequency[J]. Digital Signal Processing, 2011, 21(5): 632–637. doi: 10.1016/j.dsp.2011.03.002
    CHANG C C and LIN C J. LIBSVM: A library for support vector machines[EB/OL]. http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2018.
  • 加載中
圖(9) / 表(1)
計量
  • 文章訪問數(shù):  4067
  • HTML全文瀏覽量:  1250
  • PDF下載量:  84
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2019-03-20
  • 修回日期:  2019-06-05
  • 網(wǎng)絡(luò)出版日期:  2019-06-14
  • 刊出日期:  2019-11-01

目錄

    /

    返回文章
    返回