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Optimization-Based Wi-Fi Radio Map Construction for Indoor Positioning Using Only Smart Phones

机译:基于优化的仅使用智能手机进行室内定位的Wi-Fi无线电地图构建

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摘要

Fingerprinting-based Wi-Fi indoor positioning has great potential for positioning in GPS-denied areas. However, establishing a fingerprinting map (also called a radio map) prior to positioning (site survey) is normally a labor-intensive task. This paper proposes a method for easy site survey without need for any extra hardware. The user can conduct the site survey adopting only a smart phone. The collected inertial-based readings are processed using the pedestrian dead-reckoning algorithms to generate a raw trajectory. Then a factor graph optimization method is proposed to re-estimate the trajectory by adding constraints originated from collected Wi-Fi fingerprints and landmark positions. The proposed method is verified through an experiment in a mall. The mean positioning error is 1.10 m and the maximum error is 2.25 m. This level of positioning accuracy is considered sufficient for radio map generation purposes. A classical baseline algorithm, the k-Nearest Neighbor (kNN) algorithm, is adopted to test the positioning performance of the radio map (RM), which also validates the quality of the constructed RM from the proposed method.
机译:基于指纹的Wi-Fi室内定位在GPS受限区域中具有巨大的定位潜力。但是,在定位(现场勘测)之前建立指纹图谱(也称为无线电图)通常是一项劳动密集型任务。本文提出了一种无需任何额外硬件即可轻松进行站点调查的方法。用户只能使用智能手机进行现场调查。收集的基于惯性的读数将使用行人死区推算算法进行处理,以生成原始轨迹。然后提出了一种因子图优化方法,通过添加源自收集的Wi-Fi指纹和地标位置的约束来重新估计轨迹。通过购物中心的实验验证了所提出的方法。平均定位误差为1.10 m,最大误差为2.25 m。这种水平的定位精度被认为足以用于无线电地图的生成。采用经典的基线算法k最近邻(kNN)算法来测试无线电地图(RM)的定位性能,该算法还从所提出的方法验证了构造的RM的质量。

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