基于WiFi指纹的分层极限学习机地板检测

Atefe Alitaleshi, H. Jazayeriy, S. J. Kazemitabar
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引用次数: 11

摘要

近年来,基于室内定位服务的市场需求很大,多层建筑的精确定位受到了人们的广泛关注。在这些环境中,绝对的地板识别是准确定位的前提。本文将极限学习机(H-ELM)的层次结构应用于wifi指纹识别技术的楼层识别。该深度ELM架构包括两个部分:带无监督学习的多层特征编码(ELM-稀疏自编码器)和有监督的多类分类(原始ELM)。利用H-ELM进行地板识别比传统ELM更准确。为了评估所提出的方法,我们使用了公共UJIIndoorLoc数据集中可用的TI建筑数据。我们的模拟结果表明,使用基于wifi指纹的地板检测系统可以比其他最先进的技术实现更准确的命中率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
WiFi Fingerprinting based Floor Detection with Hierarchical Extreme Learning Machine
The indoor location-based services are high demand in the market, and precise location estimation in multi-floor buildings has received significant attention in recent years. In these environments, the absolute floor recognition is a precondition for accurate positioning. In this article, to floor determination based on the WiFi-fingerprinting technique, the hierarchical structure of extreme learning machine (H-ELM) is exploited. This deep architecture of ELM comprises of two sections: the multilayer feature encoding with unsupervised learning (ELM-sparse-autoencoder) and the supervised multiclass classification (original ELM). Floor identification using H-ELM can be more accurate than traditional ELM. For evaluating the proposed method, we utilize TI building data available in the public UJIIndoorLoc dataset. As indicated by our simulation results, using the proposed WiFi-fingerprint based floor detection system can achieve a more accurate hit rate than other state-of-the-art techniques.
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