Context Determination for Adaptive Navigation using Multiple Sensors on a Smartphone

Navigation and positioning is inherently dependent on the context, which comprises both the operating environment and the behaviour of the host vehicle or user. No single technique is capable of providing reliable and accurate positioning in all contexts. In order to operate reliably across different contexts, a multi-sensor navigation system is required to detect its operating context and reconfigure the techniques accordingly. This paper aims to determine the behavioural and environmental contexts together, building the foundation of a context-adaptive navigation system. Both behavioural and environmental context detection results are presented. A hierarchical behavioural recognition scheme is proposed, within which the broad classes of human activities and vehicle motions are detected using measurements from accelerometers, gyroscopes, magnetometers and the barometer on a smartphone by decision trees (DT) and Relevance Vector Machines (RVM). The detection results are further improved by behavioural connectivity. Environmental contexts (e.g., indoor and outdoor) are detected from GNSS measurements using a hidden Markov model. The paper also investigates context association in order to further improve the reliability of context determination. Practical test results demonstrate improvements of environment detection in context determination

Improving Environment Detection by Behaviour Association for Context Adaptive Navigation

Navigation and positioning systems depend on both the operating environment and the behavior of the host vehicle or user. The environment determines the type and quality of radio signals available for positioning, and the behavior 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 determining both environments and behaviors from smartphone sensors, serving for a context‐adaptive navigation system. Behavioral contexts cover both human activities and vehicle motions. The performance of behavior recognition in this paper is improved by feature selection and a connectivity‐dependent filter. Environmental contexts are detected from global navigation satellite system (GNSS) measurements. They are detected by using a probabilistic support vector machine, followed by a hidden Markov model for time‐domain filtering. The paper further investigates how behaviors can assist within the processes of environment detection. Finally, the proposed context‐determination algorithms are tested in a series of multicontext scenarios, showing that the proposed context association mechanism can effectively improve the accuracy of environment detection to more than 95% for pedestrian and more than 90% for vehicle

Reciprocal Estimation of Pedestrian Location and Motion State toward a Smartphone Geo-Context Computing Solution

The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed

Reciprocal estimation of pedestrian location and motion state toward a smartphone geo-context computing solution

The rapid advance in mobile communications has made information and services ubiquitously accessible. Location and context information have become essential for the effectiveness of services in the era of mobility. This paper proposes the concept of geo-context that is defined as an integral synthesis of geographical location, human motion state and mobility context. A geo-context computing solution consists of a positioning engine, a motion state recognition engine, and a context inference component. In the geo-context concept, the human motion states and mobility context are associated with the geographical location where they occur. A hybrid geo-context computing solution is implemented that runs on a smartphone, and it utilizes measurements of multiple sensors and signals of opportunity that are available within a smartphone. Pedestrian location and motion states are estimated jointly under the framework of hidden Markov models, and they are used in a reciprocal manner to improve their estimation performance of one another. It is demonstrated that pedestrian location estimation has better accuracy when its motion state is known, and in turn, the performance of motion state recognition can be improved with increasing reliability when the location is given. The geo-context inference is implemented simply with the expert system principle, and more sophisticated approaches will be developed

Investigation of Context Determination for Advanced Navigation using Smartphone Sensors

Navigation and positioning is inherently dependent on the context, which comprises 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, while the behaviour can contribute additional information to the navigation solution. Although many navigation and positioning techniques have been developed, no single one is capable of providing reliable and accurate positioning in all contexts. Therefore, it is necessary for a navigation system to be able to operate across different types of contexts. Context adaptive navigation offers a solution to this problem by detecting the operating contexts and adopting different positioning techniques accordingly. This study focuses on context determination with the available sensors on smartphone, through framework design, behavioural and environmental context detection, context association, comprehensive experimental tests, and system demonstration, building the foundation for a context-adaptive navigation system. In this thesis, the overall framework of context determination is first designed. Following the framework, the behavioural contexts, covering different human activities and vehicle motions, are recognised by different machine learning classifiers in hierarchy. Their classification results are further enhanced by feature selection and a connectivity dependent filter. Environmental contexts are detected from GNSS measurements. Indoor and outdoor environments are first distinguished based on the availability and strength of GNSS signals using a hidden Markov model based method. Within the model, the different levels of connections between environments are exploited as well. Then a fuzzy inference system is designed to enable the further classification of outdoor environments into urban and open-sky. As behaviours and environments are not completely independent, this study also considers context association, investigating how behaviours can be associated within environment detection. Tests in a series of multi-context scenarios have shown that the association mechanism can further improve the reliability of context detection. Finally, the proposed context determination system has been demonstrated in daily scenarios