The Quick Radio Fingerprint Collection Method for a WiFi-Based Indoor Positioning System

Abstract

In the WiFi-based indoor positioning system (IPS), by using WiFi access points (APs) on hand and without requiring the APs' positions, the scene analysis method has better positioning accuracy and attracts many researchers to devote their efforts to it. However, this method needs to perform a sampling process in order to collect the received signal strength indicators (RSSIs) of the APs from the places of interest to build the building's WiFi radio fingerprint database in advance. It also needs to resample frequently in order to keep the fingerprint database updated and accurate. Both tasks are time-consuming, therefore we propose a quick radio fingerprint collection (QRFC) algorithm for collecting the sampling information. The Android smartphone and its built-in motion sensors were used to help the collection process. QRFC makes the sampling process simpler, and the time needed for the sampling is close to the time of walking slowly through the path. We also propose a heuristic AP RSSI shaping algorithm to compensate for the signal attenuation caused by multi-path, shadowing, and mask effects. Several field tests were performed to compare the QRFC method to the traditional method. Experiments show that there is no significant difference in their accuracy, but QRFC takes much less time to complete.