Target/clutter disentanglement using deep adversarial training on micro-Doppler signatures

In this manuscript we argue that, under certain conditions, machine learning techniques can help to increase the signal to background level of a target signal to aid the detection and classification process in the radar signal processing chain. Specifically, the deep adversarial training concept, through the use of Denoising Adversarial Autoencoders (DAEs), has been applied for the problem of separation the micro-Doppler signatures of wind-turbine and drones, in order to be able to extract the latter for further detection and classification purposes.