Transferring Localization Models across Space
Transferring Localization Models across Space
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发表时间：
2008-07
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通讯作者：
Sinno Jialin Pan;Dou Shen;Qiang Yang;James T. Kwok
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作者：
Sinno Jialin Pan;Dou Shen;Qiang Yang;James T. Kwok
室内WiFi定位的机器学习方法涉及离线阶段和在线阶段。在离线阶段，从环境中收集数据以构建定位模型，该模型将应用于在线阶段收集的新数据以进行位置估计。然而，收集整个建筑物的标记数据将过于耗时。在本文中，我们提出了一种新的方法来将数据训练的学习模型从建筑物的一个区域转移到另一个区域。我们学习信号空间和位置空间之间的映射函数，通过解决基于流形学习技术的优化问题。在环境中的不同区域收集的数据之间共享低维流形，作为在整个环境中传播知识的桥梁。借助传递的知识，我们可以显著减少构建本地化模型所需的标记数据量。我们在一个真实的室内WiFi环境中测试我们提出的解决方案的有效性。
Machine learning approaches to indoor WiFi localization involve an offline phase and an online phase. In the offline phase, data are collected from an environment to build a localization model, which will be applied to new data collected in the online phase for location estimation. However, collecting the labeled data across an entire building would be too time consuming. In this paper, we present a novel approach to transferring the learning model trained on data from one area of a building to another. We learn a mapping function between the signal space and the location space by solving an optimization problem based on manifold learning techniques. A low-dimensional manifold is shared between data collected in different areas in an environment as a bridge to propagate the knowledge across the whole environment. With the help of the transferred knowledge, we can significantly reduce the amount of labeled data which are required for building the localization model. We test the effectiveness of our proposed solution in a real indoor WiFi environment.