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Detection of UWB ranging measurement quality for collaborative indoor positioning

Jing, Hao; Bonenberg, Lukasz Kosma; Pinchin, James; Hill, Chris; Moore, Terry

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Hao Jing

Lukasz Kosma Bonenberg

Chris Hill

Terry Moore


Wireless communication signals have become popular alternatives for indoor positioning and navigation due to lack of navigation satellite signals in such environments. The signal characteristics determine the method used for positioning as well as the positioning accuracy. Ultra-wideband (UWB) signals, with a typical bandwidth of over 1 GHz, overcome multipath problems in complicated environments. Hence, potentially achieves centimetre-level ranging accuracy in open areas. However, signals can be disrupted when placed in environments with obstructions and cause large ranging errors. This paper proposes a ranging measurement quality indicator (RQI) which detects the UWB measurement quality based on the received signal strength pattern. With a detection validity of more than 83%, the RQI is then implemented in a ranging-based collaborative positioning system. The relative constraint of the collaborative network is adjusted adaptively according to the detected RQI. The proposed detection and positioning algorithm improves positioning accuracy by 80% compared to non-adaptive collaborative positioning.


Jing, H., Bonenberg, L. K., Pinchin, J., Hill, C., & Moore, T. (in press). Detection of UWB ranging measurement quality for collaborative indoor positioning. Journal of Location Based Services, 9(4),

Journal Article Type Article
Acceptance Date Nov 4, 2015
Online Publication Date Jan 13, 2016
Deposit Date May 5, 2016
Publicly Available Date May 5, 2016
Journal Journal of Location Based Services
Print ISSN 1748-9725
Electronic ISSN 1748-9725
Publisher Taylor & Francis Open
Peer Reviewed Peer Reviewed
Volume 9
Issue 4
Keywords Collaborative positioning; adaptive; ranging constraint; quality detection; UWB ranging
Public URL
Publisher URL
Additional Information This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Location Based Services on 13/01/2016, available online:


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