Signal Fingerprint Anomaly Detection for Probabilistic Indoor Positioning

Signal fingerprinting is considered to be potential as the general indoor positioning solution since it does not require extra infrastructure or hardware modification to current customer smart devices. However, due to the complex nature of radio signal propagation, fingerprints are highly susceptible to both temporal and spatial indoor dynamics, rendering the positioning outcomes unreliable. Most recent works dedicated to efficient ways of building radio maps during the offline phase yet overlooked the localisability of fingerprints collected rather opportunistically at the online phase. In this paper, we introduce the use of pseudo-measurements and propose a fingerprint anomaly detection method that effectively evaluates the localisability of fingerprints while positioning. Empirical evaluations demonstrate that filtering out untrustworthy fingerprints can significantly improve the positioning accuracy.