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Context Detection for Advanced Self-Aware Navigation using Smartphone Sensors

Gao, H; Groves, P; (2018) Context Detection for Advanced Self-Aware Navigation using Smartphone Sensors. In: Proceedings of the International Navigation Conference 2017. Royal Institute of Navigation: Brighton, UK. Green open access

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Abstract

Navigation and positioning systems dependent on both the operating environment and the behaviour of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning and the behaviour can contribute additional information to the navigation solution. In order to operate across different contexts, a context-adaptive navigation solution introduces an element of self-awareness by detecting the operating context and configuring the positioning system accordingly. This paper presents the detection of both environmental and behavioural contexts as a whole, building the foundation of a context-adaptive navigation system. Behavioural contexts are classified using measurements from accelerometers, gyroscopes, magnetometers and the barometer by supervised machine learning algorithms, yielding an overall 95% classification accuracy. A connectivity dependent filter is then implemented to improve the behavioural detection results. Environmental contexts are detected from GNSS measurements. They are classified into indoor, intermediate and outdoor categories using a probabilistic support vector machine (SVM), followed by a hidden Markov model (HMM) used for time-domain filtering. As there will never be completely reliable context detection, the paper also shows how environment and behaviour association can contribute to reducing the chances of the context determination algorithms selecting an incorrect context. Finally, the proposed contextdetermination algorithms are tested in a series of multi-context scenarios.

Type: Proceedings paper
Title: Context Detection for Advanced Self-Aware Navigation using Smartphone Sensors
Event: the International Navigation Conference 2017
Location: Brighton, UK
Open access status: An open access version is available from UCL Discovery
Publisher version: https://rin.org.uk/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Context Detection, Smartphone Sensors, Context Association
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10062370
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