  Noise contamination affects the performance of orthogonal time frequency
space (OTFS) signals in real-world environments, making radar sensing
challenging. Our objective is to derive the range and velocity from the
delay-Doppler (DD) domain for radar sensing by using OTFS signaling. This work
introduces a two-stage approach to tackle this issue. In the first stage, we
use a convolutional neural network (CNN) model to classify the noise levels as
moderate or severe. Subsequently, if the noise level is severe, the OTFS
samples are denoised using a generative adversarial network (GAN). The proposed
approach achieves notable levels of accuracy in the classification of noisy
signals and mean absolute error (MAE) for the entire system even in low
signal-to-noise ratio (SNR) scenarios.
