Gao, H;
Groves, D;
(2018)
Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone.
Navigation
, 65
(1)
pp. 99-116.
10.1002/navi.221.
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Abstract
The signals available for navigation depend on the environment. To operate reliably in a wide range of different environments, a navigation system is required to adopt different techniques based on the environmental contexts. In this paper, an environmental context detection framework is proposed, building the foundation of a context adaptive navigation system. Different land environments are categorized into indoor, urban, and open-sky environments based on how Global Navigation Satellite System (GNSS) positioning performs in these environments. Indoor and outdoor environments are first detected based on the availability and strength of GNSS signals using a hidden Markov model. Then the further classification of outdoor environments into urban and open-sky is investigated. Pseudorange residuals are extracted from raw GNSS measurements in a smartphone and used for classification in a fuzzy inference system alongside the signal strength data. Practical test results under different kinds of environments demonstrate an overall 88.2 percent detection accuracy.
| Type: | Article |
|---|---|
| Title: | Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/navi.221 |
| Publisher version: | http://dx.doi.org/10.1002/navi.221 |
| 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. |
| UCL classification: | UCL 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/10044245 |
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