Sigma-point Kalman Smoothing for Indoor Tracking and Auto-calibration Using Time-of-flight Ranging

Anindya S. Paul, Eric A. Wan, Peter G. Jacobs

Abstract:	Reliably estimating the location of people and tracking them in an indoor environment poses a fundamental challenge for existing commercial tracking systems. We are currently developing a real time indoor location tracking system specifically for long-term monitoring of patients in a health care setting. The development is in collaboration between EmbedRF LLC and Oregon Health & Science University (OHSU). This paper focuses on the algorithmic methods being developed that are also applicable to a broad range of pedestrian monitoring and ubiquitous computing applications. At the core of our system is a sigma-point Kalman smoother (SPKS) based Bayesian inference approach. Time-of-flight (TOF) range measurements from multiple access points are fused with a model of human walking to determine a person’s 2D position and velocity. The SPKS also performs “auto-calibration” or simultaneous localization and mapping (SLAM) to determine scaling, offset, and the 2D location of the wall-mounted access points. The indoor tracking accuracy of the proposed system is better than 1 meter (m).
Published in:	Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011) September 20 - 23, 2011 Oregon Convention Center, Portland, Oregon Portland, OR
Pages:	3461 - 3469
Cite this article:	Paul, Anindya S., Wan, Eric A., Jacobs, Peter G., "Sigma-point Kalman Smoothing for Indoor Tracking and Auto-calibration Using Time-of-flight Ranging," Proceedings of the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS 2011), Portland, OR, September 2011, pp. 3461-3469.
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