Pedestrian Indoor Localization and Tracking Using Hybrid Wi-Fi/PDR for iPhones

Tuan D. Vy, Thu L. N. Nguyen, Y. Shin
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引用次数: 6

Abstract

This paper provides a hybrid approach between Wi-Fi and pedestrian dead reckoning (PDR) for the iPhones in indoor environments, and addresses the following two problems. First, since Apple Inc. no longer provides public information about currently-connected Wi-Fi (e.g., service set identifier, received signal strength and channel), Wi-Fi based pedestrian tracking apps that run on the iPhones are more restricted compared to other ones on the Android platforms. Second, even the PDR approach provides such a great way for self-localization, it suffers from accumulated errors of inertial sensors embedded in the smartphones. We propose a conversion function from a Wi-Fi status value to a proximity for the localization purpose. Then, a mobile iPhone collects mobility information from inertial measurements unit (IMU), inputs to the PDR, and combines with Wi-Fi proximity in order to perform accurate self-localization and tracking. Moreover, we improve the PDR by reducing drifting effects caused by the IMU biases. Experiment results show that the proposed scheme is effective and has low complexity, while bringing the benefits from smartphone IMU.
使用混合Wi-Fi/PDR的iphone行人室内定位和跟踪
本文为室内环境下的iphone提供了一种介于Wi-Fi和行人航位推算(PDR)之间的混合方法,并解决了以下两个问题。首先,由于苹果公司不再提供有关当前连接的Wi-Fi的公开信息(例如,服务集标识符,接收到的信号强度和频道),在iphone上运行的基于Wi-Fi的行人跟踪应用程序与Android平台上的其他应用程序相比受到更多限制。其次,即使PDR方法提供了如此好的自定位方法,它也受到智能手机中嵌入惯性传感器的累积误差的影响。我们提出了一个从Wi-Fi状态值到邻近值的转换函数,用于定位目的。然后,移动iPhone从惯性测量单元(IMU)收集移动信息,输入到PDR,并结合Wi-Fi距离,以执行精确的自我定位和跟踪。此外,我们还通过减少IMU偏差引起的漂移效应来改善PDR。实验结果表明,该方案具有较低的复杂度,同时具有智能手机IMU的优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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