Specific Emitter Identification Under Digital Pre-Distortion

ZHAO Ya-qin; XIE Dan; WU Long-wen; DING Qin-yu; HAN Yi-shen; ZHANG Zheng-hua

doi:10.12263/DZXB.20221122

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Specific Emitter Identification Under Digital Pre-Distortion

PAPERS | 更新时间：2025-12-08

- Specific Emitter Identification Under Digital Pre-Distortion
- ACTA ELECTRONICA SINICA Vol. 51, Issue 11, Pages: 3331-3342(2023)
- 作者机构：
  
  1.哈尔滨工业大学电子与信息工程学院，黑龙江哈尔滨 150001
  2.中国空间技术研究院北京空间飞行器总体设计部，北京 100094
  3.中国电子科技集团公司第十四研究所，江苏南京 210039
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20221122
  CLC： TN971.+1;
- Received：09 October 2022，
  
  Revised：2023-03-16，
  
  Published：25 November 2023
- 稿件说明：
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赵雅琴,谢丹,吴龙文等.数字预失真下的辐射源个体识别技术[J].电子学报,2023,51(11):3331-3342.

ZHAO Ya-qin,XIE Dan,WU Long-wen,et al.Specific Emitter Identification Under Digital Pre-Distortion[J].ACTA ELECTRONICA SINICA,2023,51(11):3331-3342.
赵雅琴,谢丹,吴龙文等.数字预失真下的辐射源个体识别技术[J].电子学报,2023,51(11):3331-3342. DOI： 10.12263/DZXB.20221122.

ZHAO Ya-qin,XIE Dan,WU Long-wen,et al.Specific Emitter Identification Under Digital Pre-Distortion[J].ACTA ELECTRONICA SINICA,2023,51(11):3331-3342. DOI： 10.12263/DZXB.20221122.

摘要

随着通信雷达技术的发展，针对辐射源发射机的非线性作用出现了预失真等新型技术，该类技术弱化了不同辐射源的个体特征进而恶化了辐射源个体识别性能.针对预失真下个体辐射源识别率降低的问题，本文提出了基于SincNet滤波器结构的辐射源个体识别模型.本文采用Grad-CAM方法分析残差网络类激活区域，并提取共生矩阵特征用于辐射源识别，验证了预失真后信号局部特征的有效性.随后本文提出了基于SincNet滤波器结构的辐射源个体识别算法，在降低了计算量的同时，在低信噪比下具有更高的识别精度.本文通过实验验证了数字预失真对辐射源个体识别的消极作用，并且在实测数据上的结果表明所提方法的个体识别率在信噪比0 dB下达到94%，相比本文其他先进个体识别算法有明显的提升.

Abstract

With the development of communication radar technology

new techniques such as pre-distortion have emerged to address the non-linear effects of radiation source transmitters

which weaken the individual characteristics of different radiation sources and thus deteriorate the individual source identification performance. To address the problem of reduced individual source identification under pre-distortion

this paper proposes an individual source identification model based on the SincNet filter structure. This paper uses the Grad-CAM method to analyse the residual network-like activation region and extract the co-occurrence matrix features for radiation source identification to verify the effectiveness of the local features of the signal after pre-distortion. This paper then proposes a SincNet filter structure-based algorithm for individual source identification

which reduces the computational effort while providing higher identification accuracy at low signal-to-noise ratios. The negative effect of digital pre-distortion on the individual identification of radiation sources is verified experimentally and the results on the measured data show that the individual identification rate of the proposed method reaches 94% at a signal-to-noise ratio of 0 dB

which is a significant improvement compared to other advanced individual identification algorithms in this paper.

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