Our objective is to derive the range and velocity of multiple targets from the delay-Doppler domain for radar sensing using orthogonal time frequency space (OTFS) signaling. Noise contamination affects the performance of OTFS signals in real-world environments, making radar sensing challenging. This work introduces a two-stage approach to tackle this issue. In the first stage, we use a generative adversarial network to denoise the corrupted OTFS samples, significantly improving the data quality. Following this, the denoised signals are passed to a convolutional neural network model to predict the values of the velocities and ranges of multiple targets. The proposed two-stage approach can predict the range and velocity of multiple targets, even in very low signal-to-noise ratio scenarios, with high accuracy and outperforms existing methods.
@article{arxiv.2310.00897,
title = {Practical Radar Sensing Using Two Stage Neural Network for Denoising OTFS Signals},
author = {Ashok S Kumar and Sheetal Kalyani},
journal= {arXiv preprint arXiv:2310.00897},
year = {2024}
}