Blind Universal Denoising for Radar Micro-Doppler Spectrograms Using Identical Dual Learning and Reciprocal Adversarial Training

In practice, radar measurements are hindered by unavoidable noise, which lowers the signal-to-noise ratio (SNR) and raises the problem of radar signal denoising. Thanks to the development of deep learning techniques, recently proposed denoisers are progressively capable of blind denoising. On the other hand, due to the great fitting capacity of deep neural networks, the deep-learning-based denoising model would prefer to overfit on the training set, hence diminishing the generalization of a denoiser and impeding its use in a broader situation. This article focuses on this “blind universal denoising” problem for the first time and introduces a novel generative-adversarial-network-based (GAN-based) denoiser for radar spectrograms. The core idea of the proposed model lies in minimizing the generalization error during the model’s training, and to this end, our model incorporates a proposed identical dual learning (IDL) scheme and a reciprocal adversarial training (RAT) strategy to avoid the overfitting risk in the denoiser’s training. We perform the radar simulation using a motion capture database, and verify our model’s effectiveness under three different setups of training and testing datasets. For each setup, the noise level in the training and testing sets is configured to be different so to simulate the unknown measurement situations. Eleven algorithms are selected as comparisons, and the experimental results on two criteria illustrate that our method outperforms the others with a significant improvement.