Time-based vs. Fingerprinting-based Positioning Using Artificial Neural Networks
Kirmaz A, Sahin T, Michalopoulos DS, Gerstacker W (2023)
Publication Type: Conference contribution
Publication year: 2023
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: Proceedings of the 2023 13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023
Event location: Nuremberg
ISBN: 9798350320114
DOI: 10.1109/IPIN57070.2023.10332472
Abstract
High-accuracy positioning has gained significant interest for many use-cases across various domains such as industrial internet of things (IIoT), healthcare and entertainment. Radio frequency (RF) measurements are widely utilized for user localization. However, challenging radio conditions such as non-line-of-sight (NLOS) and multipath propagation can deteriorate the positioning accuracy. Machine learning (ML)-based estimators have been proposed to overcome these challenges. RF measurements can be utilized for positioning in multiple ways resulting in time-based, angle-based and fingerprinting-based methods. Different methods, however, impose different implementation requirements to the system, and may perform differently in terms of accuracy for a given setting. In this paper, we use artificial neural networks (ANNs) to realize time-of-arrival (ToA)-based and channel impulse response (CIR) fingerprinting-based positioning. We compare their performance for different indoor environments based on real-world ultra-wideband (UWB) measurements. We first show that using ML techniques helps to improve the estimation accuracy compared to conventional techniques for time-based positioning. When comparing time-based and fingerprinting schemes using ANNs, we show that the favorable method in terms of positioning accuracy is different for different environments, where the accuracy is affected not only by the radio propagation conditions but also on the density and distribution of reference user locations used for fingerprinting.
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How to cite
APA:
Kirmaz, A., Sahin, T., Michalopoulos, D.S., & Gerstacker, W. (2023). Time-based vs. Fingerprinting-based Positioning Using Artificial Neural Networks. In Proceedings of the 2023 13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023. Nuremberg, DE: Institute of Electrical and Electronics Engineers Inc..
MLA:
Kirmaz, Anil, et al. "Time-based vs. Fingerprinting-based Positioning Using Artificial Neural Networks." Proceedings of the 13th International Conference on Indoor Positioning and Indoor Navigation, IPIN 2023, Nuremberg Institute of Electrical and Electronics Engineers Inc., 2023.
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