This paper addresses the problem of recovering the locations of both mobile devices and access points from radio signals that come in a stream manner, a problem which is called online co-localization, by exploiting both labeled and unlabeled data from mobile devices and access points. Many tracking systems function in two phases: an offline training phase and an online localization phase. In the training phase, models are built from a batch of data that are collected offline. Many of them can not cope with a dynamic environment in which calibration data may come sequentially. In such case, these systems may gradually become inaccurate without a manually costly re-calibration.
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