Pushing Bistatic Wireless Sensing toward High Accuracy at the Sub-Wavelength Scale

2026-03-08

AI Summary

This study addresses the challenge in bistatic wireless sensing where clock asynchrony induces channel phase offsets, severely degrading the accuracy of existing antenna-ratio-based methods in capturing sub-wavelength displacements. The work establishes, for the first time, a quantitative mapping between distorted channel ratios and ideal channel characteristics, and proposes a novel framework that reconstructs high-fidelity sub-wavelength displacement features using only channel magnitude information. By circumventing reliance on phase measurements, the approach effectively overcomes the performance bottleneck imposed by phase distortion. Experimental evaluations in real-world Wi-Fi and LoRa environments demonstrate that the proposed method improves sub-wavelength displacement reconstruction accuracy by nearly an order of magnitude compared to prior techniques.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• bistatic wireless sensing
• clock asynchronism
• phase offset
• sub-wavelength displacement
• channel ratio

Innovation

• bistatic wireless sensing
• sub-wavelength displacement
• channel ratio
• phase offset cancellation
• amplitude-assisted reconstruction