Chirp Signal Denoising Based on Convolution Neural Network

Many classic chirp signal processing algorithms may show significant performance degradation when the signal-to-noise ratio (SNR) is low. To address this problem, paper proposes a pre-filtering method in time-domain based on deep learning. Different from traditional filtering methods, proposed denoising convolutional neural network (DCNN) trained to recover pure noisy as much possible. Following denoising, we use two parameter estimation estimate parameters of DCNN output. The simulation results that, compared with no processing, accuracy significantly improved. This mainly due powerful extraction ability DCNN, which can improve SNR and estimation.