Calibrating RSS-Based Indoor Positioning Systems

Location estimation based on received signal strength (RSS) is the prevalent method in indoor positioning. For RSS-based methods a massive collection of training RSS samples is needed to calibrate the positioning system and to achieve a high positioning quality. The quality of these methods is directly related to the placement of the wireless sensors in the workspace and the radio map used to compute the user location. Traditionally deploying the reference points and building the radio map require human intervention and are extremely time-consuming. In this paper we aim to reduce these manual calibration efforts. We propose an automatic approach both to build a radio map in the given environment and to assess the best system calibration that fits the required positioning quality. The approach has been tested on the most used radio frequency-based technologies, i.e., IEEE 802.11 and Bluetooth.