A Deep Learning Approach to Position Estimation from Channel Impulse Responses†
1
Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 Nürnberg, Germany
2
Machine Learning and Data Analytics Lab, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, 91052 Erlangen, Germany
*
Authors to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in 9th International Conference on Indoor Positioning and Navigation, Nantes, France, 24–27 September 2018, “Convolutional Neural Networks for Position Estimation in TDoA-based Locating Systems” by Arne Niitsoo; Thorsten Edelhäußer; Christopher Mutschler.
Sensors 2019, 19(5), 1064; https://doi.org/10.3390/s19051064
Received: 31 December 2018 / Revised: 22 February 2019 / Accepted: 25 February 2019 / Published: 2 March 2019
(This article belongs to the Special Issue Sensor Fusion and Novel Technologies in Positioning and Navigation)
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.
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Keywords:
radio-based real-time locating systems; time difference of arrival; channel impulse response; time of arrival; position estimation; machine learning; deep learning; convolutional neural networks; distributed CNN
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Link: https://www.mad.tf.fau.de/files/2018/07/Convolutional-Neural-Networks-for-Position-Estimation-in-TDoA-based-Locating-Systems.pdf
Description: Convolutional Neural Networks for Position Estimation in TDoA-based Locating Systems
MDPI and ACS Style
Niitsoo, A.; Edelhäußer, T.; Eberlein, E.; Hadaschik, N.; Mutschler, C. A Deep Learning Approach to Position Estimation from Channel Impulse Responses. Sensors 2019, 19, 1064. https://doi.org/10.3390/s19051064
AMA Style
Niitsoo A, Edelhäußer T, Eberlein E, Hadaschik N, Mutschler C. A Deep Learning Approach to Position Estimation from Channel Impulse Responses. Sensors. 2019; 19(5):1064. https://doi.org/10.3390/s19051064
Chicago/Turabian StyleNiitsoo, Arne; Edelhäußer, Thorsten; Eberlein, Ernst; Hadaschik, Niels; Mutschler, Christopher. 2019. "A Deep Learning Approach to Position Estimation from Channel Impulse Responses" Sensors 19, no. 5: 1064. https://doi.org/10.3390/s19051064
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