Recalibration-free indoor localization with Wi-Fi fingerprinting of invariant received signal strength

Abstract

Localization of a human or a robot in indoor environment prior to initial human-robot interaction is the motivation of this paper. The utilization of Wi-Fi received signal strength (RSS) by adopting fingerprinting technique is used to compute the location estimation. However, Wi-Fi RSS instability incurred by mutable channel characteristics hampers a wide-spread adoption of RSS based location fingerprinting to real world applications. To overcome RSS instability problem, we propose a new method based on the concept of "invariant RSS statistics", where it is defined as the RSS samples collected at each calibration location under minimal random spatiotemporal disturbances. The invariant RSS statistics thus collected forms the reference pattern classes in the space formed by Wi-Fi sources. Fingerprinting is done by identifying the reference pattern class that maximally supports the RSS readings collected at an unknown location for available Wi-Fi sources. The support of RSS readings is defined here as the sum of the likelihood probabilities of individual RSS readings. Experimental results show that the proposed method provides superior performance to conventional ones with the success rate higher by 17%, the printing resolution finer by 30% and, naturally, no performance degradation in time without recalibration. Lastly, we present a design guideline that relates statistically the different levels of the random spatiotemporal disturbances inducing RSS instability to the minimum number of Wi-Fi sources required for achieving a certain class separation degree under the given number of calibration locations to be identified.