Device-free localization (DFL) methods use measured changes in the received signal strength (RSS) between many pairs of RF nodes to provide location estimates of a person inside the wireless network. Fundamental challenges for RSS DFL methods include having a model of RSS measurements as a function of a person's location, and maintaining an accurate model as the environment changes over time. Current methods rely on either labeled empty-area calibration or labeled fingerprints with a person at each location. Both need to be frequently recalibrated or retrained to stay current with changing environments. Other DFL methods only localize people in motion. In this paper, we address these challenges by, first, introducing a new mixture model for link RSS as a function of a person's location, and second, providing the framework to update model parameters without ever being provided labeled data from either empty-area or known-location classes. We develop two new Bayesian localization methods based on our mixture model and experimentally validate our system at three test sites with seven days of measurements. We demonstrate that our methods localize a person with non-degrading performance in changing environments, and, in addition, reduce localization error by $\mathbf {11-51}$11-51 percent compared to other DFL methods.

中文翻译：

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永远不要使用标签：在不断变化的环境中基于信号强度的贝叶斯无设备定位

无设备定位 (DFL) 方法使用多对 RF 节点之间接收信号强度 (RSS) 的测量变化来提供无线网络内人员的位置估计。RSS DFL 方法的基本挑战包括将 RSS 测​​量模型作为个人位置的函数，以及在环境随时间变化时保持准确的模型。当前的方法依赖于标记的空白区域校准或每个位置的人的标记指纹。两者都需要经常重新校准或重新培训，以适应不断变化的环境。其他 DFL 方法只能定位运动中的人。在本文中，我们通过以下方式解决这些挑战：首先，引入一个新的链接 RSS 混合模型作为一个人的位置的函数，其次，提供框架来更新模型参数，而无需提供来自空白区域或已知位置类的标记数据。我们基于我们的混合模型开发了两种新的贝叶斯定位方法，并在三个测试站点通过 7 天的测量对我们的系统进行了实验验证。我们证明了我们的方法可以在不断变化的环境中定位具有非降级表现的人，此外，通过以下方式减少定位误差$\mathbf {11-51}$11——51 与其他 DFL 方法相比的百分比。