Few-Shot Specific Emitter Identification via Integrated Complex Variational Mode Decomposition and Spatial Attention Transfer

TL;DR

This paper introduces a novel deep learning framework combining complex variational mode decomposition, spatial attention, and transfer learning to improve few-shot specific emitter identification, achieving high accuracy with limited data.

Contribution

It proposes an integrated approach that decomposes signals, models sequential features, and employs attention transfer, enabling accurate emitter identification with minimal labeled data.

Findings

Achieves 96% accuracy with only 10 symbols

Effective in limited-data scenarios

Component ablation confirms model robustness

Abstract

Specific emitter identification (SEI) utilizes passive hardware characteristics to authenticate transmitters, providing a robust physical-layer security solution. However, most deep-learning-based methods rely on extensive data or require prior information, which poses challenges in real-world scenarios with limited labeled data. We propose an integrated complex variational mode decomposition algorithm that decomposes and reconstructs complex-valued signals to approximate the original transmitted signals, thereby enabling more accurate feature extraction. We further utilize a temporal convolutional network to effectively model the sequential signal characteristics, and introduce a spatial attention mechanism to adaptively weight informative signal segments, significantly enhancing identification performance. Additionally, the branch network allows leveraging pre-trained weights from other data while reducing the need for auxiliary datasets. Ablation experiments on the simulated data demonstrate the effectiveness of each component of the model. An accuracy comparison on a public dataset reveals that our method achieves 96% accuracy using only 10 symbols without requiring any prior knowledge.