Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909135135571968 |
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| author | Eamaz, Arian Yeganegi, Farhang Hu, Yunqiao Soltanalian, Mojtaba Sun, Shunqiao |
| author_facet | Eamaz, Arian Yeganegi, Farhang Hu, Yunqiao Soltanalian, Mojtaba Sun, Shunqiao |
| contents | This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy. The mixed quantization precision significantly reduces the data amount that needs to be shared from radar nodes to the fusion center for coherent processing. We utilize the low-rank properties inherent in the constructed Hankel matrix of the mixed-precision array, to recover azimuth angles from quantized measurements. Our proposed approach addresses the challenge of mixed-quantized Hankel matrix completion, allowing for accurate estimation of the azimuth angles of interest. To evaluate the recovery performance of the proposed scheme, we establish a quasi-isometric embedding with a high probability for mixed-precision quantization. The effectiveness of our proposed scheme is demonstrated through numerical results, highlighting successful reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_08168 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion Eamaz, Arian Yeganegi, Farhang Hu, Yunqiao Soltanalian, Mojtaba Sun, Shunqiao Signal Processing This paper investigates the effects of coarse quantization with mixed precision on measurements obtained from sparse linear arrays, synthesized by a collaborative automotive radar sensing strategy. The mixed quantization precision significantly reduces the data amount that needs to be shared from radar nodes to the fusion center for coherent processing. We utilize the low-rank properties inherent in the constructed Hankel matrix of the mixed-precision array, to recover azimuth angles from quantized measurements. Our proposed approach addresses the challenge of mixed-quantized Hankel matrix completion, allowing for accurate estimation of the azimuth angles of interest. To evaluate the recovery performance of the proposed scheme, we establish a quasi-isometric embedding with a high probability for mixed-precision quantization. The effectiveness of our proposed scheme is demonstrated through numerical results, highlighting successful reconstruction. |
| title | Collaborative Automotive Radar Sensing via Mixed-Precision Distributed Array Completion |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2403.08168 |