接收域分離的跨接收系統(tǒng)通用性輻射源指紋識別
doi: 10.11999/JEIT240171
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國防科技大學(xué)電子科學(xué)學(xué)院 長沙 410073
基金項目: 國家自然科學(xué)基金(62301575)，國防科技大學(xué)青年自主創(chuàng)新科學(xué)基金(ZK2023-19)
Universal Radio Frequency Fingerprinting across Receiving Systems Using Receiving Domain Separation
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College of Electronic Science and Technology, National University of Defense Technology Changsha 410073, China
Funds: The National Natural Science Foundation of China (62301575), The Youth Independent Innovation Science Fund Project of National University of Defense Technology (ZK2023-19)
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摘要: 受輻射源硬件失真和接收機硬件失真的耦合作用，實際接收信號中帶有當(dāng)前輻射源系統(tǒng)和接收系統(tǒng)共同的“個體信息”，導(dǎo)致輻射源指紋識別技術(shù)(RFF)在跨接收系統(tǒng)場景下無法通用。為消除接收機染色效應(yīng)，該文將接收機影響作為單獨作用域，提出一種基于接收域分離的跨接收系統(tǒng)通用性輻射源指紋識別方法。該方法通過雙標(biāo)簽多通道特征聯(lián)合和域分離對抗重構(gòu)方式實現(xiàn)信號中輻射源指紋作用域與接收機染色作用域分離，利用多部接收機數(shù)據(jù)預(yù)先訓(xùn)練網(wǎng)絡(luò)對兩種作用域的分離能力，聚焦輻射源指紋信息提取，從而提升輻射源指紋識別技術(shù)在跨平臺跨接收系統(tǒng)、更新接收設(shè)備等場景下的適應(yīng)能力。相比于直接特征提取和多接收機打包訓(xùn)練方式，所提方法能夠真正適應(yīng)實際無監(jiān)督場景，且參與訓(xùn)練的源域接收機數(shù)目越多，域適應(yīng)效果越好，不需要重復(fù)訓(xùn)練即可直接推廣應(yīng)用于新接收系統(tǒng)，具有較高的實際應(yīng)用價值。
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關(guān)鍵詞:
- 輻射源指紋識別 /
- 特定輻射源識別 /
- 域分離網(wǎng)絡(luò) /
- 對抗訓(xùn)練 /
- 無監(jiān)督域適應(yīng)
Abstract: Due to the coupling effect of emitter distortion and receiver distortion, the actual received signal contains the information of the current emitter system and the receiving system, which makes the Radio Frequency Fingerprinting (RFF) technology unable to be generalized in cross-receiving system scenarios. In order to eliminate the effect of receiver, in this paper, a universal RFF method across receiving systems based on receiving domain separation is proposed which considers the influence of the receiver as a separate scope. Through the dual-label multi-channel fusion feature and domain separation adversarial reconstruction method, after trained with multi-receiver data in the source domain, the proposed method can separate domains of transmitting and receiving, extract emitter fingerprint information, which improves the generalization of RFF in scenarios such as cross-receiving system and cross-platform. Compared with the existing cross-receiver RFF methods, the proposed method can truly adapt to the actual unsupervised scenario. And the more the number of source domain receivers participating in the training, the better the domain adaptation effect. It can be directly applied to the new receiving system without repeated training, which has high practical application value. -
表 1 輻射源失真參數(shù)設(shè)置
標(biāo)簽 濾波器失真 I/Q不平衡 功率放大器 雜散單音與載頻泄露 $({a_0},{a_1},{\alpha _1})$ $({b_0},{b_1},{\beta _1})$ $G$ $\tau $ $({a_1},{a_2},{a_3})$ ${a_{{\text{ST}}}}$ ${f_{{\text{ST}}}}$ $\xi ({10^{ - 3}})$ E1 (1, 0.030, 0.25) (1, 0.030 2, 0.25) 0.999 8 –0.018 (1, 0.50, 0.30) 0.008 2 0.012 9 1.3+8.2j E2 (1, 0.060, 0.25) (1, 0.029 5, 0.25) 1.005 6 0.0175 (1, 0.08, 0.60) 0.007 5 0.013 2 1.5+7.2j E3 (1, 0.085, 0.25) (1, 0.029 0, 0.25) 1.010 2 0.012 (1, 0.01, 0.01) 0.007 0 0.012 3 1.1+6.8j E4 (1, 0.073, 0.25) (1, 0.031 0, 0.25) 0.999 2 0.003 (1, 0.01, 0.40) 0.008 7 0.013 5 1.7+9.0j E5 (1, 0.040, 0.25) (1, 0.031 3, 0.25) 0.998 2 0.024 (1, 0.60, 0.08) 0.00 90 0.011 9 2.0+6.5j 
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表 2 接收機失真參數(shù)設(shè)置
標(biāo)簽 隨機性失真 確定性失真 相位噪聲 采樣抖動 量化噪聲 濾波器失真 低噪聲放大器失真 ($\sigma _\theta ^2$,${\varOmega _0}$) $v$ $\beta $ $({a_0},{a_1},{T_{\text{a}}})$ $({b_0},{b_1},{T_{\text}})$ $({c_1},{c_2},{c_3})$ R1 (1,0.001) 0.020 1 (1,0.010,4) (1,0.031 5,4) (1,0.10,0.01) R2 (1,0.010) 0.023 2 (1,0.015,4) (1,0.030 5,4) (1,0.15,0.02) R3 (1,0.020) 0.030 3 (1,0.020,4) (1,0.029 5,4) (1,0.20,0.03) R4 (1,0.021) 0.040 4 (1,0.025,4) (1,0.028 5,4) (1,0.25,0.04) R5 (1,0.022) 0.045 5 (1,0.030,4) (1,0.027 5,4) (1,0.30,0.05) R6 (1,0.023) 0.050 6 (1,0.035,4) (1,0.026 5,4) (1,0.35,0.06) R7 (1,0.024) 0.060 7 (1,0.040,4) (1,0.025 5,4) (1,0.40,0.07) R8 (1,0.025) 0.070 3 (1,0.045,4) (1,0.024 5,4) (1,0.45,0.08) R9 (1,0.026) 0.073 3 (1,0.050,4) (1,0.023 5,4) (1,0.50,0.09) R10 (1,0.027) 0.080 3 (1,0.055,4) (1,0.022 5,4) (1,0.55,0.10)
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表 3 單接收機直接訓(xùn)練方法的正確識別率(%)
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 正確識別率 100.00 100.00 99.50 100.00 99.50 98.00 98.50 98.50 100.00 97.50
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表 4 單接收機直接訓(xùn)練方法的跨域適應(yīng)能力統(tǒng)計(%)
目標(biāo)域 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 均值 平均識別率 43.00 49.50 54.50 55.94 56.67 55.50 51.44 48.83 46.28 39.00 50.07 源域 R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 均值 平均識別率 40.39 45.67 48.11 54.11 57.11 53.11 60.22 50.50 52.44 39.00 50.07
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表 5 多接收機打包統(tǒng)一訓(xùn)練方法的正確識別率(含跨域適應(yīng))(%)
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 均值 R1 100.00 98.50 62.00 51.00 38.00 21.00 20.00 20.00 20.00 20.00 38.94 R12 100.00 100.00 99.50 89.50 66.50 47.00 29.00 21.00 20.00 20.00 49.06 R123 100.00 100.00 100.00 100.00 100.00 84.00 64.00 40.00 22.50 20.00 61.50 R1234 100.00 100.00 100.00 100.00 100.00 99.50 92.50 74.00 44.50 23.00 72.25 R12345 100.00 100.00 100.00 100.00 100.00 100.00 100.00 93.50 77.00 44.50 83.00 均值 100.00 99.70 92.30 88.10 80.90 70.30 61.10 49.70 36.80 25.50 -
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表 6 接收域分離方法的正確識別率(%)
R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 均值 R1 100.00 99.48 89.58 44.79 55.21 44.27 37.50 30.73 38.54 20.83 51.22 R12 99.48 99.48 95.31 89.06 68.75 71.88 53.13 56.25 36.98 41.15 64.06 R123 100.00 99.48 99.48 100.00 93.23 96.88 91.15 85.94 79.17 48.96 85.05 R1234 100.00 100.00 96.35 100.00 100.00 99.48 97.92 93.75 90.10 73.44 92.45 R12345 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00 均值 99.90 99.69 96.14 86.77 83.44 82.50 75.94 73.33 68.96 56.88 -
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表 7 多接收域分離方法消融實驗的正確識別率(%)
損失函數(shù) 網(wǎng)絡(luò)組成 識別率 損失函數(shù) 網(wǎng)絡(luò)組成 識別率 ${\mathcal{L}_{{\text{2 }}}} + {\mathcal{L}_{{\text{3 }}}}$ RF1+RF2+EF+RC 91.67 ${\mathcal{L}_{{\text{1 }}}} + {\mathcal{L}_{{\text{2 }}}}$ RF1+RF2+EF 98.44 ${\mathcal{L}_{{\text{2 }}}} + {\mathcal{L}_{{\text{4 }}}}$ RF1+RF2+EF+DE 99.48 ${\mathcal{L}_{{\text{2 }}}} + {\mathcal{L}_{{\text{3 }}}}$ RF1+EF+RC+DE 95.31 ${\mathcal{L}_{{\text{1 }}}} + {\mathcal{L}_{{\text{2 }}}}$ RF1+RF2+EF+DE 98.50 ${\mathcal{L}_{{\text{2 }}}}$ EF 44.50 無GRL RF1+RF2+EF+RC+DE 94.27 等權(quán)重 RF1+RF2+EF+RC+DE 93.23
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