The ubiquity of mobile devices (such as smartphones and tablets) has encouraged the development of pervasive personal navigation system (PNS) which is working in different situations and movements of a user. PNSs can provide customized navigation services in different contexts – where context is related to the user’s activity (e.g. walking mode) and the device orientation and placement. Context-aware systems are concerned with the following challenges which are addressed in this research:​ context acquisition, context abstraction and understanding, and context-aware application adaptation. The proposed context-aware PNS approach is using low-cost multi-sensor data in a multi-level sensor fusion scheme to improve the accuracy and robustness of context-aware navigation system. The Experimental results demonstrate the capabilities of the developed context-aware PNS for outdoor pedestrian navigation.

Context acquisition follows a feature-level recognition approach which includes preprocessing, feature detection, feature selection and classification step. The appropriate set of sensors and features is carefully selected to perform real-time and accurate activity recognition. Moreover, performance of different classification techniques is evaluated for context-detection in PNS.

After context acquisition, an appropriate context reasoning technique is applied to investigate integrating contexts from different sources, and finding the most accurate context. The context reasoning technique uses a fuzzy decision-level fusion algorithm to reason about the high-level context information. This method improves efficiency of context detection algorithm by applying fuzzy decision rules. These rules are acquired from various sources of information such as historical context data, expert knowledge, user preferences and constraints.

Finally, a context-aware positioning approach is developed to estimate pedestrian navigation parameters using a sensor-level fusion algorithm. In the first navigation scenario which is context-aware pedestrian dead reckoning (PDR), the performance of the PNS is improved 23% using context-aware step detection and heading alignment. In the second scenario which is vision-aided GPS navigation, position information provided by GPS is integrated with visual sensor measurements using a Kalman filter. The visual sensor measurement includes relative user’s motion (changes of velocity and heading angle) which needs device placement context information. The vision-aided GPS navigation outperforms GPS solution accuracy by 43%.