  Micro-Doppler analysis has become increasingly popular in recent years owning
to the ability of the technique to enhance classification strategies.
Applications include recognising everyday human activities, distinguishing
drone from birds, and identifying different types of vehicles. However, noisy
time-frequency spectrograms can significantly affect the performance of the
classifier and must be tackled using appropriate denoising algorithms. In
recent years, deep learning algorithms have spawned many deep neural
network-based denoising algorithms. For these methods, noise modelling is the
most important part and is used to assist in training. In this paper, we
decompose the problem and propose a novel denoising scheme: first, a Generative
Adversarial Network (GAN) is used to learn the noise distribution and
correlation from the real-world environment; then, a simulator is used to
generate clean Micro-Doppler spectrograms; finally, the generated noise and
clean simulation data are combined as the training data to train a
Convolutional Neural Network (CNN) denoiser. In experiments, we qualitatively
and quantitatively analyzed this procedure on both simulation and measurement
data. Besides, the idea of learning from natural noise can be applied well to
other existing frameworks and demonstrate greater performance than other noise
models.
