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首页> 外文期刊>International journal of ad hoc and ubiquitous computing >An adaptive Wi-Fi indoor localisation scheme using deep learning
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An adaptive Wi-Fi indoor localisation scheme using deep learning

机译：使用深度学习的自适应Wi-Fi室内定位方案

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摘要

Indoor localisation is an important issue for many indoor applications. Many deep learning-based indoor localisation schemes have been proposed. However, these existing schemes cannot adjust according to different environment. To improve the existing schemes, a novel indoor localisation scheme, which can adaptively adopt the proper fingerprint database according to the collected signals, is proposed in this paper. The proposed Wi-Fi indoor localisation scheme uses two fine-tuning algorithms, namely the cross entropy and the mean squared algorithms, to build the corresponding fingerprint databases. When the standard deviation of the collected signals does not exceed the threshold, the fingerprint database built by the cross entropy algorithm is adopted; when the standard deviation of the collected signals exceed the threshold, the fingerprint database built by the mean squared algorithm is adopted. The experimental results show that the proposed scheme can improve the accuracy of the training data and reduce the localisation error.
机译：室内本地化是许多室内应用的重要问题。已经提出了许多基于深度学习的室内定位方案。但是，这些现有方案无法根据不同的环境调整。为了改进现有方案，本文提出了一种新颖的室内定位方案，其可以根据收集的信号自适应地采用适当的指纹数据库。所提出的Wi-Fi室内定位方案使用两个微调算法，即跨熵和平均平方算法，构建相应的指纹数据库。当收集信号的标准偏差不超过阈值时，采用了跨熵算法构建的指纹数据库;当收集信号的标准偏差超过阈值时，采用了由均方算法构建的指纹数据库。实验结果表明，该方案可以提高培训数据的准确性并降低本地化误差。

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