WAIPO: A Fusion-Based Collaborative Indoor Localization System on Smartphones
WAIPO: A Fusion-Based Collaborative Indoor Localization System on Smartphones
复制标题
WAIPO：基于融合的智能手机协作室内定位系统
DOI：
10.1109/tnet.2017.2680448
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发表时间：
2017-08-01
影响因子：
3.7
通讯作者：
Duan, Lingjie
中科院分区：
文献类型：
--
作者：
Gu, Fei;Niu, Jianwei;Duan, Lingjie
基于智能手机的室内定位可以增强用户在室内环境中的体验。尽管在过去的二十年里已经提出了一些创新的解决方案，如何准确和有效地定位用户在室内环境中仍然是一个具有挑战性的问题。传统的基于Wi-Fi指纹或航位推算的室内定位系统分别遭受Wi-Fi信号的变化和航位推算的漂移问题。众包和环境传感激发了提高现有定位系统准确性的新方法。利用人的社会因素来校准定位的准确性是实用的和奖励。在本文中，我们提出了WAIPO，一个协同的室内定位系统的融合Wi-Fi和磁指纹，图像匹配，和人的共同出现。具体来说，我们可以根据Wi-Fi指纹获得最有可能的前n个位置。我们利用影像比对所知的使用者历史位置统计，提出一种照片房比对演算法，以减少估计范围。为了进一步提高定位的准确性，提出了一种共现和非共现检测算法，利用磁校准检测用户的时空共现，确定用户的位置。我们已经在Android平台上完全实现了WAIPO，并进行了测试床实验。实验结果表明，WAIPO达到了87.3%的平均准确率，这优于国家的最先进的室内定位系统。
Indoor localization based on smartphone can enhance user's experiences in indoor environments. Although some innovative solutions have been proposed in the past two decades, how to accurately and efficiently localize users in indoor environments is still a challenging problem. Traditional indoor positioning systems based on Wi-Fi fingerprints or dead reckoning suffer from the variation of Wi-Fi signals and the drift of dead reckoning problems, respectively. Crowdsourcing and ambient sensing stimulate new ways to improve existing localization systems' accuracy. Using human social factors to calibrate the accuracy of localization is practical and awarding. In this paper, we propose WAIPO, a collaborative indoor localization system with the fusion of Wi-Fi and magnetic fingerprints, image-matching, and people co-occurrence. Specifically, we could obtain the most likely top-n locations based on Wi-Fi fingerprints. We utilize the statistics of users' historical locations known by image-matching, for which we propose a photo-room matching algorithm, to reduce estimating areas. In order to further improve the accuracy of localization, we propose a co-occurrence and non-co-occurrence detection algorithm to detect users' spatial-temporal co-occurrence and determine users' locations with magnetic calibration. We have fully implemented WAIPO on the Android platform and perform testbed experiments. The experimental results demonstrate that WAIPO achieves an accuracy of 87.3% on average, which outperforms the state-of-the-art indoor localization systems.