WiFi positioning with propagation-based calibration

Abstract

Synthetic fingerprint generation using propagation models has been proposed as a cost-effective way to reduce the deployment cost of WiFi positioning systems. Contrary to traditional WiFi positioning systems, which require recording WiFi fingerprints together with ground truth locations, fingerprint generation attempts to automatically populate the radio map using theoretical properties of radio signals. Current solutions for fingerprint generation, however, are extremely complex, requiring complicated modeling of both the signal characteristics and the environment. The present paper contributes by demonstrating that simpler modeling, where only the path-loss exponent is learned from empirical measurements, is sufficient for practical purposes reaching accuracy comparable to carrying out a detailed survey.