Impact of denoising on radar target classification

This paper examines the effects of denoising on radar target identification. Three different denoising techniques are examined 1) Denoising using wavelet decomposition, 2) Denoising using Convolutional Autoencoders, and 3) Denoising using generative adversarial networks. The paper also addresses the question whether any denoising improves target recognition performance. Real stepped-frequency radar data is used to test these denoising algorithms. The data represents backscatter from commercial aircraft models as recorded in a compact range. The issue of azimuth ambiguity is also considered. Computational cost and speed are also considered. Time needed to denoise the radar backscatter is of particular importance because of the urgency to make an identification decision in a real battle scenario.