Jamming Recognition Based on Feature Fusion and Convolutional Neural Network

Abstract

The complicated electromagnetic environment of the BeiDou satellites introduces various types of external jamming to communication links, in which recognition of jamming signals with uncertainties is essential. In this work, the jamming recognition framework proposed consists of feature fusion and a convolutional neural network (CNN). Firstly, the recognition inputs are obtained by prepossessing procedure, in which the 1-D power spectrum and 2-D time-frequency image are accessed through the Welch algorithm and short-time Fourier transform (STFT), respectively. Then, the 1D-CNN and residual neural network (ResNet) are introduced to extract the deep features of the two prepossessing inputs, respectively. Finally, the two deep features are concatenated for the following three fully connected layers and output the jamming signal classification results through the softmax layer. Results show the proposed method could reduce the impacts of potential feature loss, therefore improving the generalization ability on dealing with uncertainties.

Keywords

time-frequency image feature / power spectrum feature / convolutional neural network / feature fusion / jamming recognition