Online Self-Calibration of the Propagation Model for Indoor Positioning Ranging Methods

A common problem for indoor positioning methods is the fact that the differences in the reception characteristics among devices may significantly deteriorate the performance of a positioning system. Ranging algorithms for positioning rely on the accuracy of the parameters of the propagation model. This model is used to infer an estimate of the distance of a mobile device from each access point from the Received Signal Strength Indication (RSSI). In this work we present an algorithm which dynamically recalculates and improves the propagation model. The improvement of the model parameters fits the environment's characteristics and, more importantly, the reception characteristics of the device used. The proposed algorithm is tested with different devices at an indoor deployment covering a large area where Bluetooth Low Energy (BLE) technology is used. The experimental results show that the proposed method offers a significant accuracy improvement to some devices while it slightly improves the performance of those that are more properly tuned.