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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication43rd International Conference on Telecommunications and Signal Processing (TSP)
Number of pages6
Publication statusPublished - 7 Jul 2020
Publication typeA4 Article in a conference publication
EventInternational Conference on Telecommunications and Signal Processing - Milan, Italy
Duration: 7 Jul 20209 Jul 2020


ConferenceInternational Conference on Telecommunications and Signal Processing


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.

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