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Indoor positioning based on Wi-Fi Fingerprint Technique using Fuzzy K-Nearest Neighbor

机译:基于模糊K最近邻的Wi-Fi指纹技术的室内定位

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Indoor positioning system based on Receive Signal Strength Indication(RSSI) from Wireless access equipment have become very popular in recent years. This system is very useful in many applications such as tracking service for older people, mobile robot localization and so on. While Outdoor environment using Global Navigation Satellite System(GNSS) and cellular[14] network works well and widespread for navigator. However, there was a problem with signal propagation from satellites. They cannot be used effectively inside the building areas until a urban environment. In this paper we propose the Wi-Fi Fingerprint Technique using Fuzzy set theory to adaptive Basic K-Nearest Neighbor algorithm to classify the labels of a database system. It was able to improve the accuracy and robustness. The performance of our simple algorithm is evaluated by the experimental results which show that our proposed scheme can achieve a certain level of positioning system accuracy.
机译:近年来,基于无线接入设备的接收信号强度指示(RSSI)的室内定位系统已变得非常流行。该系统在许多应用中非常有用,例如针对老年人的跟踪服务,移动机器人的本地化等。虽然使用全球导航卫星系统(GNSS)和蜂窝网络[14]的户外环境在导航器中运行良好且广泛使用。但是,来自卫星的信号传播存在问题。在城市环境之前,它们无法在建筑区域内有效使用。在本文中,我们提出了一种使用模糊集理论的Wi-Fi指纹技术,以自适应的基本K最近邻算法对数据库系统的标签进行分类。它能够提高准确性和鲁棒性。实验结果证明了该算法的性能。实验结果表明,该方案可以达到一定的定位系统精度。

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