2023/10/02 by Sheetal Kalyani, Kumar, Ashok S, Kalyani, Sheetal
Engineering · #Advanced Fiber Optic Sensors #FOS: Computer and information sciences #Information Theory (cs.IT) #PAPR reduction in OFDM #Photoacoustic and Ultrasonic Imaging
paper · pdf · doi:10.48550/arxiv.2310.00897
openalex publication_date 2023/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.