Practical Radar Sensing Using Two Stage Neural Network for Denoising OTFS Signals

TL;DR

This paper presents a two-stage neural network approach combining GANs and CNNs to denoise OTFS radar signals and accurately estimate multiple targets' range and velocity in noisy environments.

Contribution

It introduces a novel two-stage neural network framework that enhances OTFS radar sensing performance under low SNR conditions, outperforming existing methods.

Findings

Effective denoising of OTFS signals in low SNR scenarios

Accurate multi-target range and velocity estimation

Outperforms existing radar sensing techniques

Abstract

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.