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

Context Detection for Advanced Self-Aware Navigation using Smartphone Sensors