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Journal Article Walking/Non-walking and Indoor/Outdoor Cognitive-based PDR/GPS/ WiFi Integrated Pedestrian Navigation for Smartphones
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Authors
Eui Yeon Cho, Jae Uk Kwon, Seong Yun Cho, JaeJun Yoo, Seonghun Seo
Issue Date
2023-12
Citation
Journal of Positioning, Navigation, and Timing, v.12, no.4, pp.399-408
ISSN
2288-8187
Publisher
사단법인 항법시스템학회
Language
Korean
Type
Journal Article
DOI
https://dx.doi.org/10.11003/JPNT.2023.12.4.399
Abstract
In this paper, we propose a solution that enables continuous indoor/outdoor positioning of smartphone users through the integration of Pedestrian Dead Reckoning (PDR) and GPS/WiFi signals. Considering that accurate step detection affects the accuracy of PDR, we propose a Deep Neural Network (DNN)-based technology to distinguish between walking and non-walking signals such as walking in place. Furthermore, in order to integrate PDR with GPS and WiFi signals, a technique is used to select a proper measurement by distinguishing between indoor/outdoor environments based on GPS Dilution of Precision (DOP) information. In addition, we propose a technology to adaptively change the measurement error covariance matrix by detecting measurement outliers that mainly occur in the indoor/outdoor transition section through a residual-based χ2 test. It is verified through experiments on a testbed that these technologies significantly improve the performance of PDR and PDR/GPS/WiFi fingerprinting-based integrated pedestrian navigation.
KSP Keywords
Deep neural network(DNN), Dilution of Precision, Measurement errors, Outdoor environments, Outdoor positioning, Pedestrian Dead Reckoning, Step Detection, Transition section, Walking-in-place, Wi-Fi signals, WiFi fingerprinting