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Calibree: Calibration-Free Localization Using Relative Distance Estimations

机译:Calibree:使用相对距离估计无校准定位

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Existing localization algorithms, such as centroid or fingerprinting, compute the location of a mobile device based on measurements of signal strengths from radio base stations. Unfortunately, these algorithms require tedious and expensive off-line calibration in the target deployment area before they can be used for localization. In this paper, we present Calibree, a novel localization algorithm that does not require off-line calibration. The algorithm starts by computing relative distances between pairs of mobile phones based on signatures of their radio environment. It then combines these distances with the known locations of a small number of GPS-equipped phones to estimate absolute locations of all phones, effectively spreading location measurements from phones with GPS to those without. Our evaluation results show that Calibree performs better than the conventional centroid algorithm and only slightly worse than fingerprinting, without requiring off-line calibration. Moreover, when no phones report their absolute locations, Calibree can be used to estimate relative distances between phones.
机译:现有的本地化算法,例如质心或指纹,基于来自无线电基站的信号强度的测量来计算移动设备的位置。不幸的是,这些算法在目标部署区域中需要繁琐且昂贵的离线校准,然后可以用于本地化。在本文中,我们呈现Calibree,一种不需要离线校准的新型本地化算法。该算法通过基于无线电环境的签名计算移动电话对之间的相对距离来开始。然后,它将这些距离与少量GPS的手机的已知位置结合起来估计所有手机的绝对位置,有效地扩散来自带GPS的手机的位置测量。我们的评估结果表明,Calibree比传统的质心算法更好,而且只需要偏离线校准,只能略差。此外,当没有手机报告它们的绝对位置时,Calibree可用于估计手机之间的相对距离。

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