基于深度学习的自适应Wi-Fi室内定位方案

2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP) Pub Date : 2018-08-01 DOI:10.1109/APCAP.2018.8538191

Chih-Shun Hsu, Y. Chen, Tong-Ying Juang, Yi-Ting Wu

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引用次数: 1

摘要

室内定位是许多室内应用的一个重要问题。许多基于深度学习的室内定位方案已经被提出。然而，这些现有的方案不能根据不同的环境进行调整。为了改进现有的室内定位方案，本文提出了一种新的室内定位方案，该方案可以根据采集到的指纹信号自适应地选择合适的指纹库。实验结果表明，该方法能有效地减小定位误差。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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An Adaptive Wi-Fi Indoor Localization Scheme using Deep Learning

Indoor localization is an important issue for many indoor applications. Many deep learning-based indoor localization schemes have been proposed. However, these existing schemes cannot adjust according to different environment. To improve the existing schemes, a novel indoor localization scheme, which can adaptively adopt the proper fingerprint database according to the collected signals, is proposed in this paper. The experimental results show that the proposed scheme can reduce the localization error.

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2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

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