Niitsoo A, Edelhäußer T, Hadaschik N, Eberlein E, Mutschler C (2019)
Publication Language: English
Publication Type: Journal article, Original article
Publication year: 2019
Book Volume: 19
Pages Range: 1-23
Article Number: 1064
Journal Issue: 5
URI: https://www.mdpi.com/1424-8220/19/5/1064
DOI: 10.3390/s19051064
Open Access Link: https://www.mdpi.com/1424-8220/19/5/1064
Radio-based locating systems allow for a robust and continuous tracking in industrial environments and are a key enabler for the digitalization of processes in many areas such as production, manufacturing, and warehouse management. Time difference of arrival (TDoA) systems estimate the time-of-flight (ToF) of radio burst signals with a set of synchronized antennas from which they trilaterate accurate position estimates of mobile tags. However, in industrial environments where multipath propagation is predominant it is difficult to extract the correct ToF of the signal. This article shows how deep learning (DL) can be used to estimate the position of mobile objects directly from the raw channel impulse responses (CIR) extracted at the receivers. Our experiments show that our DL-based position estimation not only works well under harsh multipath propagation but also outperforms state-of-the-art approaches in line-of-sight situations.
APA:
Niitsoo, A., Edelhäußer, T., Hadaschik, N., Eberlein, E., & Mutschler, C. (2019). A Deep Learning Approach to Position Estimation from Channel Impulse Responses. Sensors, 19(5), 1-23. https://dx.doi.org/10.3390/s19051064
MLA:
Niitsoo, Arne, et al. "A Deep Learning Approach to Position Estimation from Channel Impulse Responses." Sensors 19.5 (2019): 1-23.
BibTeX: Download