Few-shot learning based blind parameter estimation for multiple frequency-hopping signals

Estimating the parameters of Frequency-Hopping (FH) signals using deep learning has attracted keen attentions in recent years. However, it is also a challenging issue as it has inadequate generalization capacity and requires a large number of annotations for network training. To overcome these limitations, we first introduce a deep learning based time-frequency ridge detection and feature generation framework which can detect the time-frequency ridges and generate separable and discriminative features for model generalization. Then, we propose a few-shot learning strategy according to the temporal relationship between adjacent frames, aiming at reducing the dependence on the number of annotations for network training. Extensive experiments demonstrate that our proposed approach can robustly estimate the parameters of multiple superimposed FH signals under noisy electromagnetic environments with only a few annotations.