Wi-Fi Indoor Localization based on Multi-Task Deep Learning

Wei Yuan Lin, Ching-Chun Huang, Nguyen Tran Duc, Hung Nguyen Manh

研究成果: Conference contribution同行評審

15 引文 斯高帕斯(Scopus)

摘要

Conventional Wi-Fi-based indoor localization methods rely on training a RSS fingerprint model to predict user locations. Most fingerprinting models only consider the distribution of RSS (radio signal strength) at a location and ignore the relationship between adjacent locations. Another challenging issue is the RSS inconsistency problem where the RSSs of neighboring locations are not as similar as the ideal expectation. To address these problems, we suggest well utilizing the richer regional features rather than the raw RSSs. Thereby, we proposed a deep learning network which integrates three components: the One-Dimension-Convolutional Neural Network to extract regional RSS features, the Siamese architecture to handle the similarity inconsistency problem, and the Regression network for user positioning. Our experiments present promising results compared with the state-of-art methods.

原文English
主出版物標題2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781538668115
DOIs
出版狀態Published - 31 1月 2019
事件23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China
持續時間: 19 11月 201821 11月 2018

出版系列

名字International Conference on Digital Signal Processing, DSP
2018-November

Conference

Conference23rd IEEE International Conference on Digital Signal Processing, DSP 2018
國家/地區China
城市Shanghai
期間19/11/1821/11/18

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