An Adaptive Deep Ensemble Learning for Specific Emitter Identification

TL;DR

This paper introduces a new deep learning framework for identifying radio transmitters using hardware-specific features, even with limited data and noise.

Contribution

ADEL introduces adaptive deep ensemble learning with heterogeneous networks and adaptive weighting for robust SEI.

Findings

ADEL outperforms existing methods in specific emitter identification tasks.

The framework is effective under limited training data and class imbalance.

Hybrid losses and adaptive weighting improve feature generalization and classification accuracy.

Abstract

Specific emitter identification (SEI), which classifies radio transmitters by extracting hardware-intrinsic radio frequency fingerprints (RFFs), faces critical challenges in noise robustness, generalization under limited training data and class imbalance. To address these limitations, we propose adaptive deep ensemble learning (ADEL)—a framework that integrates heterogeneous neural networks including convolutional neural networks (CNN), multilayer perception (MLP) and transformer for hierarchical feature extraction. Crucially, ADEL also adopts adaptive weighted predictions of the three base classifiers based on reconstruction errors and hybrid losses for robust classification. The methodology employs (1) three heterogeneous neural networks for robust feature extraction; (2) the hybrid losses refine feature space structure and preserve feature integrity for better feature generalization; and (3) collaborative decision-making via adaptive weighted reconstruction errors of the base learners for precise inference. Extensive experiments are performed to validate the effectiveness of ADEL. The results indicate that the proposed method significantly outperforms other competing methods. ADEL establishes a new SEI paradigm through robust feature extraction and adaptive decision integrity, enabling potential deployment in space target identification and situational awareness under limited training samples and imbalanced classes conditions.

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