On the Calibration and Performance of RSS-based Localization Methods

This paper analyzes the performance of several Received Signal Strength (RSS) based localization methods as a function of the calibration effort, hence as a function of deployment and maintenance costs. The deployment and maintenance costs determine the scalability and thus the applicability of a localization algorithm, and this is still a topic of research. This paper analyzes and compares the best available localization algorithms of the following localization methods: fingerprinting-, range- and proximity-based localization. An extensive amount of RSS measurements, performed in a realistic indoor environment show that range-based algorithms outperform fingerprinting and proximity-based localization algorithms when there is a limited amount of calibration measurements available. In that case, range-based algorithms have ~30% smaller errors, ~1.3 meter compared to ~1.9 meter. Our measurements show that fingerprinting-based algorithms approximate the performance of range-based algorithms as the number of calibration measurements increases from 1 to 80.