Behaviour-Aided Environment Detection for Context Adaptive Navigation

Han Gao

Peer Reviewed

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 is required to detect the operating contexts and adopt different positioning techniques accordingly. This paper focuses on detecting both environmental and behavioural contexts as a whole with smartphone sensors, serving for a context-adaptive navigation system. Behavioural contexts cover both human activities and different vehicle motions. The performance of behaviour recognition in this paper is improved by feature selection and a connectivity dependent filter. Environmental contexts are detected from GNSS measurements. They are classified into indoor, intermediate and outdoor categories using a probabilistic support vector machine, followed by a hidden Markov model used for time-domain filtering. As there will never be completely reliable context detection, the paper also shows how behaviours can be associated within environment detection to improve the reliability of context determination algorithms. Finally, the proposed context-determination algorithms are tested in a series of multi-context scenarios.
Published in: Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018)
September 24 - 28, 2018
Hyatt Regency Miami
Miami, Florida
Pages: 3283 - 3296
Cite this article: Gao, Han, "Behaviour-Aided Environment Detection for Context Adaptive Navigation," Proceedings of the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2018), Miami, Florida, September 2018, pp. 3283-3296. https://doi.org/10.33012/2018.16066
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In