Specific Emitter Identification Based on Inner–Outer Modulation Feature Fusion and SGE-LATransformer

SHI Yishuai; SHEN Lei; WANG Shoubin

doi:10.11959/j.issn.1000-0801.DXKX260085

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Specific Emitter Identification Based on Inner–Outer Modulation Feature Fusion and SGE-LATransformer

更新时间：2026-06-23

- Specific Emitter Identification Based on Inner–Outer Modulation Feature Fusion and SGE-LATransformer
- Telecommunications Science (2026)
- 作者机构：
  
  1.杭州电子科技大学通信工程学院，浙江杭州 310018
  2.中国电子科技集团公司第三十六研究所，浙江嘉兴 314000
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260085
  CLC： TN911.7
- Received：02 February 2026，
  
  Revised：2026-03-10，
  
  Accepted：01 June 2026，
- 稿件说明：
移动端阅览
SHI Yishuai, SHEN Lei, WANG Shoubin. Specific Emitter Identification Based on Inner–Outer Modulation Feature Fusion and SGE-LATransformer[J/OL]. Telecommunications Science, 2026.
DOI：

SHI Yishuai, SHEN Lei, WANG Shoubin. Specific Emitter Identification Based on Inner–Outer Modulation Feature Fusion and SGE-LATransformer[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260085.

摘要

针对复杂电磁环境下民航通信辐射源个体识别中特征判别性不足、抗噪性能受限及未知个体识别能力下降的问题，提出一种基于内外调制特征融合与 SGE-LATransformer 的辐射源个体识别方法。针对AM-MSK复合调制民航信号中外调制与内调制均由发射链路不同硬件环节共同作用形成、且其物理机制与环境敏感性存在差异的特点，分别构建原始信号时频图以刻画功率放大、幅度控制及包络整形等外调制相关硬件引入的整体调制特征，以及解除外调制后的 MSK 内调制时频图以突出由振荡器、调制器和滤波器非理想性所决定的内在调制特征，并通过拼接融合形成内外调制联合时频特征表示。该融合特征在时频域实现了外调制硬件差异与内调制硬件指纹特征的互补建模，从民航信号调制机理出发，对辐射源个体特征进行了深入挖掘。进一步地，设计引入Spatial Group Enhance（SGE）模块的局部感知Transformer网络（SGE-LATransformer），通过全局—局部特征协同建模及组级空间注意力机制，突出稳定的个体判别区域并抑制噪声引入的无关响应。实验结果表明，所提方法在识别精度、抗噪性能及未知个体识别能力方面均优于传统单一时频特征、双谱及差分双谱方法以及常规深度学习模型，验证了其在民航通信辐射源个体识别任务中的有效性与工程实用价值。

Abstract

To address the problems of insufficient feature discriminability

limited noise robustness

and degraded unknown-emitter identification performance in civil aviation communication specific emitter identification under complex electromagnetic environments

a specific emitter identification method based on inner–outer modulation feature fusion and an SGE-LATransformer was proposed. Considering that

in AM–MSK composite-modulated civil aviation signals

both outer modulation and inner modulation were jointly formed by different hardware stages along the transmitting chain while exhibiting distinct physical mechanisms and environmental sensitivities

the time–frequency representation of the original signal was constructed to characterize the overall modulation characteristics introduced by outer-modulation-related hardware components such as power amplifiers

amplitude control units

and envelope shaping circuits

and the time–frequency representation of the MSK inner modulation

obtained after removing the outer modulation

was constructed to highlight the intrinsic modulation characteristics determined by the non-idealities of oscillators

modulators

and filters; these representations were concatenated to form a joint time–frequency feature of inner and outer modulation. The fused representation enabled complementary modeling of outer-modulation hardware discrepancies and inner-modulation hardware fingerprint features in the time–frequency domain

thereby facilitating in-depth characterization of individual emitter signatures from the perspective of civil aviation signal modulation mechanisms. Furthermore

a local-aware Transformer network incorporating the Spatial Group Enhance (SGE) module

referred to as the SGE-LATransformer

was designed

in which global–local feature collaborative modeling and a group-level spatial attention mechanism were employed to emphasize stable emitter-discriminative regions while suppressing noise-induced irrelevant responses. Experimental results demonstrated that the proposed method outperformed traditional single time–frequency features

bispectrum and differential bispectrum methods

as well as conventional deep learning models in terms of identification accuracy

noise robustness

and unknown-emitter identification capability

validating its effectiveness and engineering practicality for civil aviation communication specific emitter identification.

关键词

Keywords

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