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.

翻译：利用无线通信信号进行非接触式感知，因其非侵入特性和无处不在的基础设施而受到广泛关注。尽管前景广阔，但无线通信固有的双站部署会引入时钟不同步，导致信道响应中存在未知相位偏移，从而阻碍了细粒度感知。现有先进系统广泛采用跨天线信道比来消除这些有害的相位偏移。然而，信道比仅在目标位移为整数波长时能保持感知特征的精度，而会损失亚波长尺度的保真度。为了克服这一限制，我们首次推导了畸变的比值特征与理想信道特征之间的定量映射关系。基于此，我们开发了一个鲁棒的框架，该框架利用信道响应幅度从畸变的比值中恢复出理想信道特征。在Wi-Fi和LoRa上的真实世界实验表明，我们的方法能有效重建亚波长位移细节，实现了近一个数量级的精度提升。