MVL-Tra: Multi-view LFM signal source classification using Transformer

Abstract

Classifying the sources of linear frequency modulation (LFM) signals has practical significance, as these signals are widely applied in various scenarios. However, the signals emitted by the same configured sources exhibit insignificant differences and may suffer severe degradation in low signal-to-noise ratio (SNR) conditions. Previous research failed to achieve satisfactory classification results in these two conditions. To address such challenges, we propose a signal source classification method for LFM signals using multi-view representations, based on the Transformer network, named MVL-Tra. Multi-view learning improves robustness by utilizing multiple feature sets and multi-dimensional analysis on these features. Transformer, a emerging deep neural network, enhances the distinctiveness of features between signals through capturing long-range dependencies when dealing with large-scale time-series data. In the experiment, we conducted classification on a dataset comprising 15 sets of low-SNR LFM signals. Notably, Even under the challenging condition of SNR = −5, our method achieved an accuracy of 93.78%. Th results confirm the effectiveness of using multi-view representations and Transformer for LFM signal source classification.