|Many smartphone-based indoor tracking methods exist today, but to ensure consumer acceptance of such technologies, it is critical that these systems minimize their effect on the battery life of the device. Signal fingerprinting methods can provide good performance with low processing overhead but require prior surveying. Methods exploiting opportunistic sensing and machine learning techniques such as Simultaneous Localization and Mapping (SLAM) need no prior data but come at the cost of high computational load. This paper proposes a smartphone-based indoor positioning system that exploits a new intelligent filtering approach to reduce this computational load. ’SmartSLAM’ moves between different sensor fusion algorithms depending on the current level of certainty in the system, maintaining good positioning performance with low computational load, and improving battery life. This paper introduces two core algorithms underpinning SmartSLAM: the Fingerprint EKF (FEKF) and Fingerprint EKF Smoother (FEKFS).
|NAVIGATION: Journal of the Institute of Navigation, Volume 62, Number 1
|55 - 72
|Cite this article:
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