Machine Learning for Practical Localization System Using Multiview CSI

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.