Research on an Adaptive Algorithm for Indoor Bluetooth Positioning

Building structure and other factors lead to the performance deterioration of global postioning system (GPS) positioning systems indoors. An adaptive model for Bluetooth-based indoor positioning is proposed in this paper, targeting at the complex indoor environment, to improve the performance of Bluetooth-oriented indoor positioning systems. More accurate Received Signal Strength Indicator (RSSI) calibration which is optimized via Gaussian filtering, together with the environment-dependent attenuation coefficient optimization, results in a more precise hybrid model in the complicated indoor environment. Experiment results show that the difference between the estimated results and the measured samples is less than 0.25m as the target node and reference node is less than 1.5m far from each other. As the distance increases to more than 1.5m, the relative difference between the estimated values and the measured ones decreases to 7.8% at most, satisfying the requirements for indoor positioning applications.