期刊名称：International Journal of Networking and Computing
印刷版ISSN：2185-2847
出版年度：2018
卷号：8
期号：2
页码：328-340
语种：English
出版社：International Journal of Networking and Computing
摘要：In this paper, we propose a new method to estimate a traveling direction for a pedestrian with a smart phone by using the Inertial Measurement Unit (IMU) and principal component analysis (PCA). Our estimator does not restrict a holding status of the smart phone. It is possible to estimate an attitude of the smart phone based on the IMU filter which makes use of values from sensors embedded in itself. We estimate a traveling direction of the pedestrian based on the PCA by using the output of the filter. However, this method essentially includes an indeterminacy problem which allows a direction reversal because the component of the PCA is non-directed graph. In this paper, we investigate a modified traveling direction estimation method in order to suppress a performance degradation caused by this problem. From experimental results, we show that the proposed estimator outperforms the conventional one.
其他摘要：In this paper, we propose a new method to estimate a traveling direction for a pedestrian with a smart phone by using the Inertial Measurement Unit (IMU) and principal component analysis (PCA). Our estimator does not restrict a holding status of the smart phone. It is possible to estimate an attitude of the smart phone based on the IMU filter which makes use of values from sensors embedded in itself. We estimate a traveling direction of the pedestrian based on the PCA by using the output of the filter. However, this method essentially includes an indeterminacy problem which allows a direction reversal because the component of the PCA is non-directed graph. In this paper, we investigate a modified traveling direction estimation method in order to suppress a performance degradation caused by this problem. From experimental results, we show that the proposed estimator outperforms the conventional one.
关键词：Pedestrian dead reckoning; Traveling direction estimation; Inertial measurement unit; Principal Component Analysis
