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Few-Shot Specific Emitter Identification Based on Variational Mode Decomposition and Meta-Learning

Recently, deep learning has become the mainstream solution to solve specific emitter identification (SEI) problems. However, because large amounts of labeled signal samples cannot be obtained in noncooperative scenarios, the performance of deep learning-based data-driven methods for SEI was limited. As a result, a novel SEI method targeted on few-shot was proposed in this study. First, the received signal was preprocessed based on variational mode decomposition and the Hilbert analysis to obtain the Hilbert time-frequency spectrum. Subsequently, a classification neural network model was built and trained with a small number of Hilbert time-frequency spectrum samples through meta-learning. This model could identify specific emitters with limited training samples. The experimental results showed that this method accomplishes network training with as few as 80 training samples while obtaining a good level of generalization and effectively identifying different emitter individuals. In addition, this method exhibits a strong robustness to noise by maintaining an identification accuracy of more than 80% in channels with low signal-to-noise ratios. Finally, the proposed method demonstrated better identification performances than other existing methods with its capability to effectively solve SEI problems in the few-shot scenario.