Context Determination for Adaptive Navigation using Multiple Sensors on a Smartphone

Gao, Han; Groves, Paul D.

doi:10.33012/2016.14573

Cited by 24 publications

(35 citation statements)

References 31 publications

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“…However, on the basis of the findings in Radu et al and Gao and Groves, these two features are not sufficient for a reliable indoor/outdoor classification. In particular, it is difficult to distinguish “shallow indoor” and “deep urban” scenarios from each other with only these two features.…”

Section: Environmental Context Detectionmentioning

confidence: 98%

“…An implementation of pattern recognition algorithms for behavior recognition has been described in detail in Gao and Groves . In the paper, a hierarchical framework was proposed to distinguish both pedestrian activities and vehicle motions.…”

Section: Behavioral Context Recognitionmentioning

confidence: 99%

“…In Gao and Groves, different time‐domain and frequency‐domain features were extracted from sensor measurements. They are summarized in Table .…”

Section: Behavioral Context Recognitionmentioning

confidence: 99%

“…The SFFS algorithm was implemented for behavioral recognition, by using the same dataset and the same corresponding machine learning algorithms for each classifier described in Gao and Groves . Figure shows the average classification accuracy of the three classifiers as a function of the number of features selected by SFFS.…”

Section: Behavioral Context Recognitionmentioning

confidence: 99%

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Improving environment detection by behavior association for context‐adaptive navigation

Gao

Groves

2020

NAVIGATION

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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.

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Section: Environmental Context Detectionmentioning

confidence: 98%

Section: Behavioral Context Recognitionmentioning

confidence: 99%

“…In Gao and Groves, different time‐domain and frequency‐domain features were extracted from sensor measurements. They are summarized in Table .…”

Section: Behavioral Context Recognitionmentioning

confidence: 99%

Section: Behavioral Context Recognitionmentioning

confidence: 99%

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Improving environment detection by behavior association for context‐adaptive navigation

Gao

Groves

2020

NAVIGATION

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“…A simple approach is to use the C/N 0 of the strongest received signals to rescale all of the C/N 0 measurements to match an open‐environment model. Alternatively, behavioral context detection techniques can be used to detect the placement of mobile device . Environmental context detection is also useful for determining when to use 3DMA GNSS; the technique is not designed to be used indoors and conventional GNSS positioning is adequate in open environments.…”

Section: Recommendations For 3dma Gnss Algorithm Enhancementsmentioning

confidence: 99%

Performance assessment of 3D ‐mapping‐aided GNSS part 2: Environment and mapping

et al. 2019

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A full performance assessment of 3D‐mapping‐aided (3DMA) Global Navigation Satellite System (GNSS) in dense urban areas is presented. This second part of a two‐part paper focusses on the effects of the surrounding environment and 3D‐mapping, based on data collected in London using a u‐blox EVK M8T GNSS receiver. Conventional GNSS, shadow matching, 3DMA ranging, and integrated 3DMA GNSS all perform best when the proportion of directly visible sky is high, the building height to street width ratio is low, and the average building height is below 20 m. 3DMA GNSS methods demonstrate maximum benefit at sky visibilities of 15% to 35%. All methods exhibit poorer accuracy in environments dominated by glass and steel buildings. Temporary features, such as large buses and lorries, also degrade 3DMA accuracy. Using full 3D city models gives significantly higher accuracy than simple block models, and missing buildings lead to larger positioning errors. Further enhancements to the 3DMA GNSS algorithms are recommended.

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Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone

Gao

Groves

2018

J Inst Navig

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Context Determination for Adaptive Navigation using Multiple Sensors on a Smartphone

Cited by 24 publications

References 31 publications

Improving environment detection by behavior association for context‐adaptive navigation

Improving environment detection by behavior association for context‐adaptive navigation

Performance assessment of 3D ‐mapping‐aided GNSS part 2: Environment and mapping

Environmental Context Detection for Adaptive Navigation using GNSS Measurements from a Smartphone

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