Influence of Predicted and Measured Fingerprint on the Accuracy of RSSI-based Indoor Location Systems

Abstract

WLAN indoor location that is based on received signal strength indication (RSSI) technique needs extensive calibration to build a signal fingerprint. Re-calibration is also needed if there is a major change in the propagation environment. The use of propagation models to predict signal fingerprint becomes an interesting preposition. This paper will investigate the influence of predicted fingerprint on the accuracy of indoor location. They include empirical propagation models (i.e. one-slope model and multi-wall model) and a semi-deterministic model. A framework for indoor location with the nearest-neighbour and particle filter are developed to evaluate predicted and measured fingerprints. In order to take advantage of environment description, a map-filtering technique is also elaborated.