改進(jìn)變分模態(tài)分解與多特征的通信輻射源個體識別方法
doi: 10.11999/JEIT231348
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西安理工大學(xué)自動化與信息工程學(xué)院 西安 710048
基金項目: 國家自然科學(xué)基金(61671375)
Individual Identification Method for Communication Emitters Based on Improved Variational Modal Decomposition and Multiple Features
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School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Funds: The National Natural Science Foundation of China (61671375)
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摘要: 針對通信輻射源指紋特征難以提取和單一特征識別率不高的問題，并考慮到通信輻射源細(xì)微特征的非線性、非平穩(wěn)特點，該文提出了一種基于改進(jìn)變分模態(tài)分解和多特征的通信輻射源個體識別方法。首先，為了獲得變分模態(tài)分解的分解層數(shù)和懲罰因子的最優(yōu)組合，采用鯨魚優(yōu)化算法對通信輻射源符號波形信號的變分模態(tài)分解方法進(jìn)行了改進(jìn)，該方法以序列復(fù)雜度為停止準(zhǔn)則，使每個符號波形信號能夠自適應(yīng)地分解出包含非線性指紋特征的高頻信號分量和數(shù)據(jù)信息的低頻分量；然后，根據(jù)相關(guān)閾值選取能夠最佳表征輻射源非線性特征的高頻信號分量層數(shù)，分別對其提取模糊熵、排列熵、Higuchi維數(shù)以及Katz維數(shù)并組成多域聯(lián)合特征向量；最后，通過卷積神經(jīng)網(wǎng)絡(luò)實現(xiàn)通信輻射源個體識別分類，利用ORACLE公開數(shù)據(jù)集進(jìn)行實驗。實驗結(jié)果表明：該方法有較高的識別精度且具有良好的抗噪聲性能。
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關(guān)鍵詞:
- 通信輻射源個體識別 /
- 變分模態(tài)分解 /
- 非線性指紋特征 /
- 卷積神經(jīng)網(wǎng)絡(luò)
Abstract: Aiming at the difficulties in extracting fingerprint features from communication emitters and the low recognition rate of single features, considering the nonlinear and non-stationary characteristics of subtle features of communication emitters, this paper proposes an individual identification method for communication emitters based on improved variational mode decomposition and multiple features. Firstly, in order to obtain the optimal combination of decomposition levels and penalty factors for variational mode decomposition, the variational modal decomposition of communication emitter symbol waveform signals is improved with whale optimization algorithm, in which the sequence complexity is used as the stopping criterion in this method to enable each symbol waveform signal to adaptively decompose several high-frequency signal components containing nonlinear fingerprint features and low-frequency components of data information; Then, according to the relevant threshold, the number of high-frequency signal component layers is selected that can best represent the nonlinear characteristics of the radiation source and the fuzzy entropy, permutation entropy, Higuchi dimension, and Katz dimension are extracted to form a multi-domain joint feature vector; Finally, the recognition and classification of communication emitters are achieved through convolutional neural networks, and recognition and classification experiments are conducted using the Oracle public dataset. The experimental results show that this method has high recognition accuracy and good noise immunity. -
表 1 不同層數(shù)在不同信噪比下的識別率(%)
層數(shù) 信噪比SNR(dB) –4 dB –2 dB 0 dB 2 dB 4 dB 3 66.5 69.5 72.1 73.4 75.3 4 64.2 71 73.2 80.3 81 5 67.1 73.8 76 83.1 89 6 69.5 77.9 82 89.8 96.3 7 68.8 78 83.3 89.1 92.6 
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[1] HUANG Guangquan, YUAN Yingjun, WANG Xiang, et al. Specific emitter identification based on nonlinear dynamical characteristics[J]. Canadian Journal of Electrical and Computer Engineering, 2016, 39(1): 34–41. doi: 10.1109/CJECE.2015.2496143. [2] 任東方, 張濤, 韓潔. 結(jié)合ITD與非線性分析的通信輻射源個體識別方法[J]. 信號處理, 2018, 34(3): 331–339. doi: 10.16798/j.issn.1003-0530.2018.03.010.REN Dongfang, ZHANG Tao, and HAN Jie. Approach of specific communication emitter identification combining ITD and nonlinear analysis[J]. Journal of Signal Processing, 2018, 34(3): 331–339. doi: 10.16798/j.issn.1003-0530.2018.03.010. [3] XIE Yang, WANG Shilian, ZHANG Eryang, et al. Specific emitter Identification based on nonlinear complexity of signal[C]. 2016 IEEE International Conference on Signal Processing, Communications and Computing, Hong Kong, China, 2016: 1–6. doi: 10.1109/ICSPCC.2016.7753733. [4] LU N, ZHOU T X, WEI J F, et al. Application of a whale optimized variational mode decomposition method based on envelope sample entropy in the fault diagnosis of rotating machinery[J]. Measurement Science and Technology, 2022, 33(1): 015014. doi: 10.1088/1361-6501/ac3470. [5] ELMAGHBUB A and HAMDAOUI B. Leveraging hardware-impaired out-of-band information through deep neural networks for robust wireless device classification[J]. arXiv: 2004.11126, 2020. doi: 10.48550/arXiv.2004.11126. [6] BREMNES K, MOEN R, YEDURI S R, et al. Classification of UAVs utilizing fixed boundary empirical wavelet sub-bands of RF fingerprints and deep convolutional neural network[J]. IEEE Sensors Journal, 2022, 22(21): 21248–21256. doi: 10.1109/JSEN.2022.3208518. [7] ZHANG Junqing, WOODS R, SANDELL M, et al. Radio frequency fingerprint identification for narrowband systems, modelling and classification[J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 3974–3987. doi: 10.1109/TIFS.2021.3088008. [8] HUANG Yi, HU Aiqun, FAN Jiayi, et al. Joint estimation of transmitter IQ imbalance and nonlinearity with multipath in OFDM systems[C]. 2023 IEEE 98th Vehicular Technology Conference, Hong Kong, China, 2023: 1–6. doi: 10.1109/VTC2023-Fall60731.2023.10333512. [9] DRAGOMIRETSKIY K and ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531–544. doi: 10.1109/TSP.2013.2288675. [10] MIRJALILI S and LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51–67. doi: 10.1016/j.advengsoft.2016.01.008. [11] ZAHID M U, NISAR M D, and SHAH M H. Radio frequency fingerprint extraction based on multiscale approximate entropy[J]. Physical Communication, 2022, 55: 101927. doi: 10.1016/j.phycom.2022.101927. [12] BANDT C and POMPE B. Permutation entropy: A natural complexity measure for time series[J]. Physical Review Letters, 2002, 88(17): 174102. doi: 10.1103/PhysRevLett.88.174102. [13] CHEN Weiting, WANG Zhizhong, XIE Hongbo, et al. Characterization of surface EMG signal based on fuzzy entropy[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(2): 266–272. doi: 10.1109/TNSRE.2007.897025. [14] KATZ M J. Fractals and the analysis of waveforms[J]. Computers in Biology and Medicine, 1988, 18(3): 145–156. doi: 10.1016/0010-4825(88)90041-8. [15] SHAMSI E, AHMADI-PAJOUH M A, and SEIFI ALA T. Higuchi fractal dimension: An efficient approach to detection of brain entrainment to theta binaural beats[J]. Biomedical Signal Processing and Control, 2021, 68: 102580. doi: 10.1016/j.bspc.2021.102580. [16] SANKHE K, BELGIOVINE M, ZHOU Fan, et al. No radio left behind: Radio fingerprinting through deep learning of physical-layer hardware impairments[J]. IEEE Transactions on Cognitive Communications and Networking, 2020, 6(1): 165–178. doi: 10.1109/TCCN.2019.2949308. [17] DENG Shouyun, HUANG Zhitao, WANG Xiang, et al. Radio frequency fingerprint extraction based on multidimension permutation entropy[J]. International Journal of Antennas and Propagation, 2017, 2017(1): 1538728. doi: 10.1155/2017/1538728. [18] SUN Liting, WANG Xiang, YANG Afeng, et al. Radio frequency fingerprint extraction based on multi-dimension approximate entropy[J]. IEEE Signal Processing Letters, 2020, 27: 471–475. doi: 10.1109/LSP.2020.2978333. [19] HE Boxiang and WANG Fanggang. Cooperative specific emitter identification via multiple distorted receivers[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 3791–3806. doi: 10.1109/TIFS.2020.3001721. -
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