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MapSentinel: Can the Knowledge of Space Use Improve Indoor Tracking Further?

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara 06800, Turkey
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Sensors 2016, 16(4), 472;
Received: 25 January 2016 / Revised: 21 March 2016 / Accepted: 26 March 2016 / Published: 2 April 2016
(This article belongs to the Section Physical Sensors)
PDF [2892 KB, uploaded 7 April 2016]


Estimating an occupant’s location is arguably the most fundamental sensing task in smart buildings. The applications for fine-grained, responsive building operations require the location sensing systems to provide location estimates in real time, also known as indoor tracking. Existing indoor tracking systems require occupants to carry specialized devices or install programs on their smartphone to collect inertial sensing data. In this paper, we propose MapSentinel, which performs non-intrusive location sensing based on WiFi access points and ultrasonic sensors. MapSentinel combines the noisy sensor readings with the floormap information to estimate locations. One key observation supporting our work is that occupants exhibit distinctive motion characteristics at different locations on the floormap, e.g., constrained motion along the corridor or in the cubicle zones, and free movement in the open space. While extensive research has been performed on using a floormap as a tool to obtain correct walking trajectories without wall-crossings, there have been few attempts to incorporate the knowledge of space use available from the floormap into the location estimation. This paper argues that the knowledge of space use as an additional information source presents new opportunities for indoor tracking. The fusion of heterogeneous information is theoretically formulated within the Factor Graph framework, and the Context-Augmented Particle Filtering algorithm is developed to efficiently solve real-time walking trajectories. Our evaluation in a large office space shows that the MapSentinel can achieve accuracy improvement of 31.3% compared with the purely WiFi-based tracking system. View Full-Text
Keywords: indoor tracking systems; non-intrusive; map-aided; WiFi; ultrasonic sensor networks; particle filters indoor tracking systems; non-intrusive; map-aided; WiFi; ultrasonic sensor networks; particle filters

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Jia, R.; Jin, M.; Zou, H.; Yesilata, Y.; Xie, L.; Spanos, C. MapSentinel: Can the Knowledge of Space Use Improve Indoor Tracking Further? Sensors 2016, 16, 472.

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