Indoor radio location algorithm using empirical propagation models and probability distribution heuristics

Abstract

Research is conducted at the Budapest University of Technology in the field of indoor positioning using radio waves, with practical application issues in focus. Our goal is to enhance and combine existing algorithms and create an implementation that is efficient enough to enable real-time operation in 3D space in multi-level office environments while retaining the accuracy of more complex systems. The proposed solution is based on proven empirical propagation models (the Motley-Keenan Model and the New Empirical Model of Cheung, Sau and Murch), with the ability to augment and refine with surveyed radio fingerprint data. The propagation model described in this paper can be used to generate estimated signal strength patterns for locations that were left out of the survey; and the values coming from the two different sources can be further handled in a common way. An algorithm is proposed to calculate a probability distribution over the floor plan using these expected values and the positioning-time measurements. The results from this location algorithm are combined with other probability distributions, generated by heuristics such as the distribution of walls, previous walking directions and typical indoor movement patterns, as well as data from G-sensors and digital compasses built into modern handheld devices. Other real-world issues are investigated like reaction to changes in the placement of radio beacons, and the comparability of various devices with different signal characteristics. The proposed algorithm can achieve an average positioning error of 3 metres in a common office environment using fingerprinting data, and errors are only about 50%greater when relying solely on simulated signal strength values.