Adversarial unsupervised domain adaptation for cross scenario waveform recognition

Abstract

Deep Learning based waveform recognition has become a topical area of research recently. In fact, due to complex and heterogeneous scenarios, it is impossible to train a universal model for all waveform recognition tasks. Unsupervised domain adaptation (UDA) has an ability to take advantage of knowledge from an available source dataset and apply it to an unlabeled target dataset. Therefore, we propose a novel waveform recognition method based on an adversarial UDA, which incorporates adversarial learning to improve the cross scenario recognition performance. Specifically, a two-channel convolutional neural network is well designed for waveform recognition task and the system gradually converges an optimized stable state via two innovative methods, namely Adversarial unsupervised domain adaptation with gradient reversal (GR-AUDA) and Feature augmentation adversarial unsupervised domain adaptation (FA-AUDA). Furthermore, we sampled a more abundant waveform dataset under various scenarios, including typical indoor, outdoor, and corridor cases, using software defined radio. We evaluate our method on both public datasets and our own datasets. The results demonstrate that our proposed adversarial UDA framework is able to significantly improve the recognition performance of target domain waveforms.