Improving the Precision of Wireless Localization Algorithms: ML Techniques for Indoor Positioning

Abstract

Due to the tremendous increase in the number of wearable devices and proximity-based services, the need for improved indoor localization techniques becomes more significant. The evolution of the positioning from a hardware perspective is pacing its way along with various software-based approaches also powered by Machine Learning (ML). In this paper, we apply ML algorithms to the real-life collected signal parameters in an indoor localization system based on Ultra-Wideband (UWB) technology to make an analysis of the signal and classify it accordingly. The contribution aims to answer the question of whether an indoor positioning system could benefit from utilizing ML for signal parameter analysis in order to increase its location accuracy, reliability, and robustness across various environments. To this end, we compare different applications of ML approaches and detail the trade-off between computational speed and accuracy.