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
