A Deep Learning Approach to Position Estimation from Channel Impulse Responses

Beitrag in einer Fachzeitschrift
(Originalarbeit)


Details zur Publikation

Autor(en): Niitsoo A, Edelhäußer T, Hadaschik N, Eberlein E, Mutschler C
Zeitschrift: Sensors
Jahr der Veröffentlichung: 2019
Band: 19
Heftnummer: 5
Seitenbereich: 1-23
ISSN: 1424-8220
Sprache: Englisch


Abstract

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.


FAU-Autoren / FAU-Herausgeber

Mutschler, Christopher Dr.-Ing.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


Zitierweisen

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: 

Zuletzt aktualisiert 2019-02-03 um 12:53