基于深度自適應(yīng)小波網(wǎng)絡(luò)的通信輻射源個(gè)體識(shí)別
網(wǎng)絡(luò)安全與數(shù)據(jù)治理 2023年第5期
劉高輝，于文濤
（西安理工大學(xué)自動(dòng)化與信息工程學(xué)院，陜西西安710048）
摘要： 針對(duì)現(xiàn)有的通信輻射源個(gè)體識(shí)別方法中人工提取特征復(fù)雜以及深度學(xué)習(xí)網(wǎng)絡(luò)的識(shí)別機(jī)制缺乏清晰解釋的問(wèn)題，提出了一種基于深度自適應(yīng)小波網(wǎng)絡(luò)(Deep Adaptive Wavelet Network，DAWN)的通信輻射源個(gè)體識(shí)別方法。首先分析了選擇互調(diào)干擾作為輻射源間個(gè)體特征的原因；接著應(yīng)用了可實(shí)現(xiàn)提升小波變換的卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)去提取特征，并在其基礎(chǔ)上設(shè)計(jì)出可以同時(shí)完成特征提取和識(shí)別的DAWN；最后，選擇Oracle數(shù)據(jù)集驗(yàn)證方法的可行性。實(shí)驗(yàn)結(jié)果表明：利用DAWN對(duì)5個(gè)通信輻射源個(gè)體識(shí)別的準(zhǔn)確率為95.5%，并且方法具有良好的抗噪性。
中圖分類號(hào)：TN911.7
文獻(xiàn)標(biāo)識(shí)碼：A
DOI:10.19358/j.issn.2097-1788.2023.05.012
引用格式：劉高輝，于文濤.基于深度自適應(yīng)小波網(wǎng)絡(luò)的通信輻射源個(gè)體識(shí)別［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2023，42（5）：71-77.
文獻(xiàn)標(biāo)識(shí)碼：A
DOI:10.19358/j.issn.2097-1788.2023.05.012
引用格式：劉高輝，于文濤.基于深度自適應(yīng)小波網(wǎng)絡(luò)的通信輻射源個(gè)體識(shí)別［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)治理，2023，42（5）：71-77.
Individual recognition of communication radiation source based on depth adaptive wavelet network
Liu Gaohui，Yu Wentao
(Automation and Information Academy，Xi'an University of Technology，Xian 710048，China)
Abstract： Aiming at the problem of the complex artificial features extracted in the existing individual recognition methods of communication radiation sources and the lack of clear interpretation of the recognition mechanism of deep learning networks, an individual recognition method of communication radiation sources based on Deep Adaptive Wavelet Network (DAWN) is proposed. Firstly, the intermodulation interference is analyzed as the reason for individual characteristics between radiation sources. Then, the convolutional neural network structure that can realize lifting wavelet transform is applied to extract features, based on which DAWN can complete feature extraction and recognition at the same time. Finally, Oracle data sets are selected to verify the feasibility of the method. The experimental results show that the accuracy of identification of 5 communication radiation sources by DAWN is 955%, and the method has good antinoise performance.
Key words : specific emitter identification；lifting wavelet transform；depth adaptive wavelet network
0 引言
隨著物聯(lián)網(wǎng)和通信技術(shù)的發(fā)展，無(wú)線設(shè)備呈現(xiàn)出指數(shù)級(jí)的增長(zhǎng)態(tài)勢(shì)，未來(lái)海量的敏感機(jī)密數(shù)據(jù)將在無(wú)線設(shè)備間傳輸，所以對(duì)通信輻射源進(jìn)行個(gè)體識(shí)別對(duì)保證無(wú)線通信網(wǎng)絡(luò)中的信息安全有著重要的實(shí)際意義。
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作者信息：
劉高輝，于文濤
（西安理工大學(xué)自動(dòng)化與信息工程學(xué)院，陜西西安710048）
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