Achieving Accurate Modulated Signal Recognition: A Hybrid Neural Network Approach With Data Augmentation

Abstract

Accurate classification of radar signals remains a key challenge in automatic modulation classification (AMC), particularly in scenarios with limited training data and complex signal variations. To address this, we propose a novel hybrid neural architecture and incorporate a magnitude rescaling method for data augmentation. Specifically, our hybrid neural structure integrates a bidirectional long short-term memory (Bi-LSTM) network, a dynamic feature extraction module, and a transformer encoder in a cascaded structure. It effectively processes one-dimensional signals enhanced via the proposed random magnitude rescaling method. Experimental results demonstrate our approach achieves a competitive classification accuracy of 94.18% on the RML2016a data set and exhibits strong performance on a hardware-in-the-loop simulation dataset. The implementation of our radar signal modulation classification method, along with the related datasets, is available at: https://github.com/stu-cjlu-sp/rsrc-for-pub/tree/main/ASEFEAMC.