Practical Radar Sensing Using Two Stage Neural Network for Denoising OTFS Signals
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929502458740736 |
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| author | Kumar, Ashok S Kalyani, Sheetal |
| author_facet | Kumar, Ashok S Kalyani, Sheetal |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_00897 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Practical Radar Sensing Using Two Stage Neural Network for Denoising OTFS Signals Kumar, Ashok S Kalyani, Sheetal Information Theory 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. |
| title | Practical Radar Sensing Using Two Stage Neural Network for Denoising OTFS Signals |
| topic | Information Theory |
| url | https://arxiv.org/abs/2310.00897 |