Lightweight and robust indoor positioning
The aim of this project is to develop a positioning system that works reliably across users, motion modes and device types, is flexible to make the most of available resources (sensor data and maps), and is capable of learning and improving over time. The envisaged system will be able to work flexibly with or without bespoke positioning infrastructure. It should be lightweight enough to run on the user's mobile device without having to offload the position computation to the cloud. The basis of the positioning system is a robust pedestrian dead reckoning algorithm that works accurately for different motion modes (texting, hand swinging, device in trouser / shirt pocket, and so on) despite the noisy nature of inertial measurement units embedded in smart devices. In the presence of environment maps (e.g. floorplans, radio or magnetic fingerprint maps), the positioning system should be able to exploit them to correct the drift of the inertial trajectory, while improving the quality of the available maps as it acquires more data. Finally, the positioning system should be able to learn and improve its accuracy the more it is employed by a user in a particular setting.
Selected Publications
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Non−line−of−sight identification and mitigation using received signal strength
Zhuoling Xiao‚ Hongkai Wen‚ Andrew Markham‚ Niki Trigoni‚ Phil Blunsom and J. Frolik
In IEEE Transactions on Wireless Communications. 2015.
Details about Non−line−of−sight identification and mitigation using received signal strength | BibTeX data for Non−line−of−sight identification and mitigation using received signal strength | Download (pdf) of Non−line−of−sight identification and mitigation using received signal strength
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Lightweight map matching for indoor localization using conditional random fields (BEST PAPER)
Zhuoling Xiao‚ Hongkai Wen‚ Andrew Markham and Niki Trigoni
In The International Conference on Information Processing in Sensor Networks (IPSN'14). Berlin‚ Germany. 2014.
Details about Lightweight map matching for indoor localization using conditional random fields (BEST PAPER) | BibTeX data for Lightweight map matching for indoor localization using conditional random fields (BEST PAPER) | Link to Lightweight map matching for indoor localization using conditional random fields (BEST PAPER)
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Demo: Lightweight continuous indoor tracking system
Zhuoling Xiao‚ Hongkai Wen‚ Andrew Markham and Niki Trigoni
In 11th European Conference on Wireless Sensor Networks (EWSN'14). Oxford‚ UK. 2014.
Details about Demo: Lightweight continuous indoor tracking system | BibTeX data for Demo: Lightweight continuous indoor tracking system | Download (pdf) of Demo: Lightweight continuous indoor tracking system
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