Range-based indoor user localization using reflected signal path model

5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011) Pub Date : 2011-06-30 DOI:10.1109/DEST.2011.5936634

Jongdae Jung, H. Myung

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引用次数: 7

Abstract

User localization is one of the key technologies for mobile robots to successfully interact with humans. Among various localization methods using radio frequency (RF) signals, time of arrival (TOA) based localization is popular since the target coordinates can be directly calculated from the accurate range measurements. In complex indoor environment, however, RF ranging-based localization is quite challenging since the range measurements suffer not only from signal noise but also from signal blockages and reflections. A set of range measurements taken in complex indoor environment verifies that almost all measurements are non-line-of-sight (NLOS) ranges which have striking difference to the line-of-sight (LOS) distances. These NLOS range measurements make severe degradation in the accuracy of trilateration based localizations if used without any compensation. In this paper we propose a particle filter-based localization algorithm which utilizes indoor geometry from a given map to estimate the NLOS signal path and compensates for the range measurements. The algorithm is verified with experiments performed in real indoor environments.

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基于距离的反射信号路径模型的室内用户定位

用户定位是移动机器人成功与人交互的关键技术之一。在各种利用射频信号的定位方法中，基于到达时间(TOA)的定位方法由于可以直接从精确的距离测量中计算目标坐标而广受欢迎。然而，在复杂的室内环境中，基于射频距离的定位具有很大的挑战性，因为距离测量不仅受到信号噪声的影响，而且还受到信号阻塞和反射的影响。一组在复杂室内环境下的距离测量验证了几乎所有的测量值都是非视距(NLOS)范围，这与视距(LOS)距离有显著差异。如果没有任何补偿，这些NLOS范围测量会严重降低基于三边定位的精度。本文提出了一种基于粒子滤波的定位算法，该算法利用给定地图的室内几何形状来估计NLOS信号路径并对距离测量进行补偿。在真实的室内环境中进行了实验验证。

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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5th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2011)

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