SCIENCE@home

A Deep Learning Approach to Position Estimation from Channel Impulse Responses †

Arne Niitsoo,Thorsten Edelhäußer,E. Eberlein,N. Hadaschik,Christopher Mutschler

Published 2019 in Italian National Conference on Sensors

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.

PUBLICATION RECORD

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-79 of 79 references · Page 1 of 1

CITED BY

Showing 1-62 of 62 citing papers · Page 1 of 1