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A New Paradigm for Device-free Indoor Localization: Deep Learning with Error Vector Spectrum in Wi-Fi Systems by Liu, Wen; Hsiao, An-Hung; Shen, Li-Hsiang; Feng, Kai-Ten is a scholarly article available to read on EtoBox.

What is A New Paradigm for Device-free Indoor Localization: Deep Learning with Error Vector Spectrum in Wi-Fi Systems about?

The demand for device-free indoor localization using commercial Wi-Fi devices has rapidly increased in various fields due to its convenience and versatile applications. However, random frequency offset (RFO) in wireless channels poses challenges to the accuracy of indoor localization when using fluctuating channel state information (CSI). To mitigate the RFO problem, an error vector spectrum (EVS) is conceived thanks to its higher resolution of signal and robustness to RFO. To address these challenges, this paper proposed a novel error vector assisted learning (EVAL) for device-free indoor localization. The proposed EVAL scheme employs deep neural networks to classify the location of a person in the indoor environment by extracting ample channel features from the physical layer signals. We conducted realistic experiments based on OpenWiFi project to extract both EVS and CSI to examine the performance of different device-free localization techniques. Experimental results show that our proposed EVAL scheme outperforms conventional machine learning methods and benchmarks utilizing either CSI amplitude or phase information. Compared to most existing CSI-based localization schemes, a ne

Author

Liu, Wen; Hsiao, An-Hung; Shen, Li-Hsiang; Feng, Kai-Ten

Published

2023

Language

EN