Optimized Beamforming for Joint Bistatic Positioning and Monostatic Sensing
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913717583609856 |
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| author | Zhang, Yuchen Chen, Hui Zheng, Pinjun Ning, Boyu Wymeersch, Henk Al-Naffouri, Tareq Y. |
| author_facet | Zhang, Yuchen Chen, Hui Zheng, Pinjun Ning, Boyu Wymeersch, Henk Al-Naffouri, Tareq Y. |
| contents | We investigate the performance tradeoff between \textit{bistatic positioning (BP)} and \textit{monostatic sensing (MS)} in a multi-input multi-output orthogonal frequency division multiplexing scenario. We derive the Cramér-Rao bounds (CRBs) for BP at the user equipment and MS at the base station. To balance these objectives, we propose a multi-objective optimization framework that optimizes beamformers using a weighted-sum CRB approach, ensuring the weak Pareto boundary. We also introduce two mismatch-minimizing approaches, targeting beamformer mismatch and variance matrix mismatch, and solve them distinctly. Numerical results demonstrate the performance tradeoff between BP and MS, revealing significant gains with the proposed methods and highlighting the advantages of minimizing the weighted-sum mismatch of variance matrices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11392 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimized Beamforming for Joint Bistatic Positioning and Monostatic Sensing Zhang, Yuchen Chen, Hui Zheng, Pinjun Ning, Boyu Wymeersch, Henk Al-Naffouri, Tareq Y. Signal Processing We investigate the performance tradeoff between \textit{bistatic positioning (BP)} and \textit{monostatic sensing (MS)} in a multi-input multi-output orthogonal frequency division multiplexing scenario. We derive the Cramér-Rao bounds (CRBs) for BP at the user equipment and MS at the base station. To balance these objectives, we propose a multi-objective optimization framework that optimizes beamformers using a weighted-sum CRB approach, ensuring the weak Pareto boundary. We also introduce two mismatch-minimizing approaches, targeting beamformer mismatch and variance matrix mismatch, and solve them distinctly. Numerical results demonstrate the performance tradeoff between BP and MS, revealing significant gains with the proposed methods and highlighting the advantages of minimizing the weighted-sum mismatch of variance matrices. |
| title | Optimized Beamforming for Joint Bistatic Positioning and Monostatic Sensing |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2501.11392 |