Time-Frequency Image Enhancement of Frequency Modulation Signals by Using Fully Convolutional Networks

The uncertainty principle and cross-term can lead to blur, fake signal components and energy oscillation in time-frequency distribution, deteriorate the results of signal tracking, radar/sonar imaging and parameter estimation. Hence in this paper, we propose a time-frequency image enhancement method based on convolutional neural networks for clearer instantaneous frequency curve. The training data are generated by a frequency modulation signal generator, and then an end-to-end training is performed between Wigner-Ville distributions and time-frequency images. Our networks not only extract underlying features of Wigner-Ville distribution, but also understand the semantic of instantaneous frequency curve and use the priori knowledge of the modulation mode. Therefore, it can correctly recognize and eliminate the cross-terms, and transform the Wigner-Ville distribution to an image that can accurate represent the instantaneous frequency curve. The method is tested by three kinds of frequency modulation signals randomly with Gaussian noise. The results show that it can work properly in most cases and has the generalization ability of multi-component signals.