Applying t-Distributed Stochastic Neighbor Embedding for Improving Fingerprinting-Based Localization System

Getaneh Berie Tarekegn, Li Chia Tai*, Hsin Piao Lin, Belayneh Abebe Tesfaw, Rong Terng Juang, Huan Chia Hsu, Kai Lun Huang, Kanishk Singh

*此作品的通信作者

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

Large-scale location estimation is crucial for many artificial intelligence Internet of Things (IoT) applications in the era of smart cities. This letter proposes a deep learning-based outdoor positioning scheme for large-scale wireless settings using fingerprinting techniques. We first developed a feature extraction technique using t-distributed stochastic neighbor embedding (t-SNE) to extract dominant and distinguishable features while eliminating noises from the radio fingerprints. Afterward, we developed a deep learning-based coarse-fine localization framework to improve positioning performance significantly. Based on our numerical analysis, the proposed scheme reduces computation time by 64.41%, and the average positioning error is 38 cm. Therefore, the proposed approach significantly improved positioning accuracy and reduced computation time.

原文English
文章編號6005004
期刊IEEE Sensors Letters
7
發行號9
DOIs
出版狀態Published - 1 9月 2023

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