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Unsupervised Learning for Crowdsourced Indoor Localization in Wireless Networks

机译:无线网络中众包室内本地化的无监督学习

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摘要

Wireless Local Area Network (WLAN) location fingerprinting has become a prevalent approach to indoor localization. However, its widespread adoption has been hindered by the need for manual efforts to collect location-labeled fingerprints for the calibration of a localization model. Several semi-supervised learning methods have been applied to reduce such manual efforts by exploiting unlabeled fingerprints, but they still require some amount of labeled fingerprints for initializing the learning process. In this research, in order to obviate the need for location labels or references, we propose a novel unsupervised learning method that calibrates a localization model using unlabeled fingerprints based on a hybrid global-local optimization scheme. The method determines the optimal placement of fingerprint sequences on an indoor map, under the constraint imposed by the inner structure shown on the map such as walls and partitions. An efficient interaction between a global and a local optimization in the hybrid scheme drastically reduces the complexity of the learning task. Experiments carried out in a single- and a multi-story building revealed that the proposed method could successfully build a precise localization model without any location reference or explicit efforts to collect labeled samples.
机译:无线局域网(WLAN)位置指纹识别已成为室内定位的一种普遍方法。但是,它的广泛采用受到需要手动努力来收集用于定位模型校准的位置标记指纹的阻碍。已应用了几种半监督学习方法来通过利用未标记的指纹来减少此类手动工作,但它们仍需要一定数量的已标记的指纹才能初始化学习过程。在这项研究中,为了避免使用位置标签或参考,我们提出了一种新的无监督学习方法,该方法基于混合全局-局部优化方案使用未标记的指纹来校准定位模型。该方法在由地图上显示的内部结构(例如墙壁和分区)施加的约束下,确定指纹序列在室内地图上的最佳放置。混合方案中的全局优化和局部优化之间的有效交互可大大降低学习任务的复杂性。在单层和多层建筑物中进行的实验表明,该方法可以成功建立精确的定位模型,而无需任何位置参考或明确的工作来收集标记的样本。

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