A field test of parametric WLAN-fingerprint-positioning methods

Fingerprint-based (FP) positioning methods determine a receiver's position using a database of radio signal strength measurements that were collected earlier at known locations. For positioning with WLAN signals, nonparametric methods such as the weighted k-nearest neighbour (WKNN) method are widely used. Due to their large data storage and transmission requirements those methods are infeasible for large-scale mobile device services. In this paper we consider parametric FP methods, which use model-based representations of the survey data. We analyse the positioning performance of those methods using real-world WLAN indoor data and compare the results to those of the WKNN method.