Abstract
Various machine learning techniques on indoor localization using radio signals are being rapidly developed to achieve a sub-meter accuracy under noisy and complex environments. A fingerprint database using channel state information (CSI) extracted from a radio packet based on an orthogonal frequency diversity multiplexing (OFDM) channel can provide enough information to localize a transmitter device with a neural network (NN) based machine learning technique. In this article, we concern about the more practical use of the localization system using machine learning. We introduce a novel design of a signal preprocessing method for NN fingerprinting. To deal with the real building environment with corridors where certain signals cannot arrive at the receiver, our preprocessing with nonnegative matrix factorization (NMF) recovers multiview CSI of the original signal and complete the sparse CSI matrix, which enables robust and practical localization. The recovered CSI is then applied to variational inference-based machine learning that finds informative corridor views among multiview CSI. Our proposed system significantly outperforms other existing machine learning-based systems and shows a localization accuracy of 89 cm, while it still maintains the reliable accuracy even with 30% sparse network. It is the first time to consider how to design a practical localization system in an empirical building environment.
Highlights
A precise analysis of radio signals gives us an opportunity to find the accurate location of the radio device
To let our system train only for the informative line of sight (LoS) signals and obtain better localization accuracy, we refer to [5] using variational inference [4], which was first used for variational autoencoder (VAE) [33], along with reparameterization scheme [34]
In this article, we discussed the practicality of the localization system using channel state information (CSI) on the WiFi orthogonal frequency diversity multiplexing (OFDM) channel
Summary
A precise analysis of radio signals gives us an opportunity to find the accurate location of the radio device. Leads to about 5 m outdoor localization accuracy [1], indoor localization requires more accurate positioning of the transmitter devices Such a localization system locates a transmitter based on signal information received from multiple receivers. In such an environment, the wall blocks a certain line of sight (LoS) communication path of a signal and the remaining non-line of sight (NLoS) paths make the received CSI non-informative or useless for localization. Even if there is 30% sparsity on the radio communication network, our novel preprocessing successfully recovers the lost CSIs and assures 1.11 m of localization accuracy, which still improves the best-known system with no sparsity
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