行人定位使用WiFi指纹和脚装惯性传感器

Yang Gu, Caifa Zhou, A. Wieser, Zhimin Zhou
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引用次数: 14

摘要

足部惯性定位(FMIP)和基于指纹的WiFi室内定位(FWIP)是两种很有前途的室内定位解决方案。然而,长期来看,FMIP存在累积的定位误差,而FWIP则涉及到非常劳动密集型的离线培训阶段。本文提出了一种结合这两种方法的新方法,该方法可以限制FMIP的误差增长,并且不需要任何离线现场测量阶段。该方法在粒子滤波器框架中实现,其中每个粒子表示用户的潜在轨迹,并根据其在信号强度空间中的一致性进行加权。与传统的基于高斯过程的方法相比,该方法的计算量更小,并且不受位置域中任何先验信息的影响,如接入点的位置、特定位置处的接收信号强度等。通过实验验证了该方法与传统的基于高斯过程的方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pedestrian positioning using WiFi fingerprints and a foot-mounted inertial sensor
Foot-mounted inertial positioning (FMIP) and fingerprinting based WiFi indoor positioning (FWIP) are two promising solutions for indoor positioning. However, FMIP suffers from accumulative positioning errors in the long term while FWIP involves a very labor-intensive offline training phase. A new approach combining the two solutions is proposed in this paper, which can limit the error growth in FMIP and is free of any offline site survey phase. This approach is realized in the framework of a particle filter, where each particle denotes a potential trajectory of the user and is weighted according to its consistency in signal strength space. Compared with the traditional Gaussian process based approaches, the proposed one has less computational cost and is free from any prior information in the position domain, such as the positions of access points, received signal strengths at certain positions and so on. An experiment is carried out to demonstrate the performance of the proposed approach compared to the traditional Gaussian process based approach.
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