Abstract
ABSTRACTIndoor navigation provides the positioning service to the indoor users, where the GPS coverage is not available. The challenges for most signal-based indoor positioning systems are the unpredictable signal propagation caused by the complex building interiors, and the dynamic of the environment caused by the peoples' movements. However, most existing systems made no assumption about the quality of their predictions, which is crucial in such noisy indoor environment. To address this challenge, this article proposes a confidence measure to reflect the uncertainty of the positioning prediction. More importantly, the users may control the size of the prediction set by setting the confidence level tailoring to their personal requirement. The proposed approach in this article has been validated in three real office buildings with challenging indoor environments, which indicated that it performed up to 20% more accurate than traditional Naïve Bayes and Weighted K-nearest neighbours (W-KNN) algorithms.
Highlights
Global Navigation Satellite Systems (GNSS) such as GPS have been successfully deployed in the past two decades, and are indispensable for outdoor navigation
This article proposes a confidence measure to reflect the uncertainty of the positioning prediction
The proposed approach in this article has been validated in three real office buildings with challenging indoor environments, which indicated that it performed up to 20% more accurate than traditional Naïve Bayes and Weighted K-nearest neighbours (W-KNN) algorithms
Summary
Global Navigation Satellite Systems (GNSS) such as GPS have been successfully deployed in the past two decades, and are indispensable for outdoor navigation. Overall, based on how the systems interact with the indoor environment, they can be divided into two broad categories, which are infrastructure-based systems and infrastructure-free systems With the former, the system relies on a piece of hardware that needs to be installed into the building. The latter are selfcontained and require no additional changes to the indoor environment These systems piggyback on top of the structures that already exist in the building (e.g. the WiFi network) to provide the positioning service. Most existing fingerprint-based systems made no assumption about the quality of their predictions To address this challenge, this article proposes a confidence measure to reflect the uncertainty of the positioning prediction. Two WiFi fingerprinting datasets collected by the author in real office environments are introduced for further researches
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