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首页> 外文会议>IEEE 10th International Conference on Ubiquitous Intelligence and Computing; IEEE 10th International Conference on Autonomic and Trusted Computing >Constructing Adaptive Indoor Radio Maps for Dynamic Wireless Environments
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Constructing Adaptive Indoor Radio Maps for Dynamic Wireless Environments

机译：为动态无线环境构建自适应室内无线电地图

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摘要

In received signal strength fingerprints based indoor localization systems, the radio map built by labeled wireless fingerprints is easily outdated over time, while re-calibrating the overall radio map is time consuming. To avoid the tedious task, we propose to employ manifold alignment to label the current radio map from outdated radio map, with the constraint of the Hidden Markov Model trained by trajectories of the received signal strength readings. Manifold alignment can align the low-dimensional manifold structures of two different data sets and transfer knowledge across them. Transition matrix generated by Hidden Markov Model is used to constrain the alignment of manifolds. The proposed algorithms are tested in a real world ZigBee environment. Experiment results show that our method outperforms state-of-the-art transfer learning algorithms.
机译：在基于接收信号强度指纹的室内定位系统中，由标记的无线指纹建立的无线电图很容易随着时间而过时，而重新校准整个无线电图则非常耗时。为避免繁琐的任务，我们建议采用歧管对齐方式从过时的无线电地图中标记当前的无线电地图，并通过接收信号强度读数的轨迹训练隐马尔可夫模型的约束。流形对齐可以对齐两个不同数据集的低维流形结构，并在它们之间传递知识。隐马尔可夫模型产生的过渡矩阵被用来约束流形的对准。所提出的算法在真实的ZigBee环境中进行了测试。实验结果表明，我们的方法优于最新的转移学习算法。

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