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Passive Light Spectral Indoor Localization by Jiawei Hu; Yanxiang Wang; Hong Jia; Wen Hu; Mahbub Hassan; Ashraf Uddin; Brano Kusy; Moustafa Youssef is a scholarly article available to read on EtoBox.

What is Passive Light Spectral Indoor Localization about?

We propose a novel Visible Light Positioning (VLP) method, called Iris, that uses light spectral information (LSI) to localize humans completely passively in the sense that it neither requires the user to carry any device, nor does it require any modifications to existing lighting infrastructure. Iris localizes a user based on the interference they produce on the LSI recorded at an array of spectral sensors embedded in the environment. We design a deep neural network that can effectively learn location fingerprints directly from the sensor LSI data and predict locations accurately under varying lighting conditions. We prototype Iris using a commercial-off-theshelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength channels. We benchmark Iris against the state-of-the-art passive VLPs that rely on conventional photo-sensors capable of measuring only a single light intensity value aggregated over the entire visible spectrum. Our evaluations over two typical indoor environments, a 25 m 2 one-bedroom apartment and a 13m × 8m office space, demonstrate that Iris can significantly reduce both the localization errors and the number of required sen

Author

Jiawei Hu; Yanxiang Wang; Hong Jia; Wen Hu; Mahbub Hassan; Ashraf Uddin; Brano Kusy; Moustafa Youssef

Publisher

ACM

Published

2022

Language

EN