The Performance Analysis of Time Series Data Augmentation Technology for Small Sample Communication Device Recognition

Abstract

Communication device recognition is a key problem of electromagnetic space perception. At present, the traditional recognition technology is difficult to adapt to the complex signal situation. Thanks to the deep learning's superior capability of processing complex and massive data, it has been a hot topic in the field of communication area. However, it needs a large amount and high-quality signal dataset, which will pay for much cost. Therefore, small sample and data augmentation technology should be given much attention. In this article, we propose a novel time series data augmentation technology for small sample recognition. First, a complex neural network is designed to recognize the communication device based on the in-phase/quadrature time series. Second, based on signal data characteristic, several simple and effective methods of time series data augmentation are analyzed, which include noise disturbance, amplitude and time-delay transformation, frequency offset, and phase shift transformation. Third, in order to get a better augmentation result, based on complex neural network model characteristic, a novel data augmentation method of virtual adversarial training is presented for the small sample device recognition. Finally, a series of experimental simulations in real ADS-B signal indicates that the proposed method is suitable for the time series analysis and recognition of communication device with a small sample.