WiFi-Based Relative Localization with Weak Supervision
A team of researchers has introduced the Intersection Pathway (IP), a novel framework for relative localization that utilizes WiFi fingerprints through cross-modal learning. In contrast to conventional absolute positioning techniques that depend on extensive coordinate labeling, this method calculates the displacement between two WiFi fingerprint traces without the need for absolute position predictions. It leverages weak supervision derived from stepwise motion vectors captured via inertial sensing. The framework integrates fingerprint traces (f-traces) and displacement traces (d-traces) within a unified latent space, ensuring that additive structures reflect physical motion composition. Experiments conducted on synthetic data validate the effectiveness of this approach.
Key facts
- The paper studies relative localization, estimating displacement between two WiFi fingerprint traces.
- Weak supervision is used via stepwise motion vectors from inertial sensing.
- Intersection Pathway (IP) aligns f-traces and d-traces in a shared latent space.
- Additive structure in latent space enables direct relative-displacement inference.
- Experiments were conducted on synthetic data.
- The approach avoids dense coordinate annotations required by absolute positioning methods.
- The paper is published on arXiv with ID 2605.16357.
- The research is categorized under cross-modal learning.
Entities
Institutions
- arXiv