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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.07492 |
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| _version_ | 1866918378667507712 |
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| author | Li, Wenwei Zhou, Jiarun Quan, Qinxiao Zhang, Fusang Zhang, Daqing |
| author_facet | Li, Wenwei Zhou, Jiarun Quan, Qinxiao Zhang, Fusang Zhang, Daqing |
| contents | Contactless sensing using wireless communication signals has garnered significant attention due to its non-intrusive nature and ubiquitous infrastructure. Despite the promise, the inherent bistatic deployment of wireless communication introduces clock asynchronism, which leads to unknown phase offsets in channel response and hinders fine-grained sensing. State-of-the-art systems widely adopt the cross-antenna channel ratio to cancel these detrimental phase offsets. However, the channel ratio preserves sensing feature accuracy only at integer-wavelength target displacements, losing sub-wavelength fidelity. To overcome this limitation, we derive the first quantitative mapping between the distorted ratio feature and the ideal channel feature. Building on this foundation, we develop a robust framework that leverages channel response amplitude to recover the ideal channel feature from the distorted ratio. Real-world experiments across Wi-Fi and LoRa demonstrate that our method can effectively reconstruct sub-wavelength displacement details, achieving nearly an order-of-magnitude improvement in accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07492 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Pushing Bistatic Wireless Sensing toward High Accuracy at the Sub-Wavelength Scale Li, Wenwei Zhou, Jiarun Quan, Qinxiao Zhang, Fusang Zhang, Daqing Information Theory Emerging Technologies Human-Computer Interaction Machine Learning Contactless sensing using wireless communication signals has garnered significant attention due to its non-intrusive nature and ubiquitous infrastructure. Despite the promise, the inherent bistatic deployment of wireless communication introduces clock asynchronism, which leads to unknown phase offsets in channel response and hinders fine-grained sensing. State-of-the-art systems widely adopt the cross-antenna channel ratio to cancel these detrimental phase offsets. However, the channel ratio preserves sensing feature accuracy only at integer-wavelength target displacements, losing sub-wavelength fidelity. To overcome this limitation, we derive the first quantitative mapping between the distorted ratio feature and the ideal channel feature. Building on this foundation, we develop a robust framework that leverages channel response amplitude to recover the ideal channel feature from the distorted ratio. Real-world experiments across Wi-Fi and LoRa demonstrate that our method can effectively reconstruct sub-wavelength displacement details, achieving nearly an order-of-magnitude improvement in accuracy. |
| title | Pushing Bistatic Wireless Sensing toward High Accuracy at the Sub-Wavelength Scale |
| topic | Information Theory Emerging Technologies Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2603.07492 |