A Deep Learning Approach to Position Estimation from Channel Impulse Responses

Sensors

Arne Niitsoo¹  Thorsten Edelhäußer¹  Ernst Eberlein¹  Christopher Mutschler¹  Niels Hadaschik¹ 

[1] Machine Learning and Information Fusion Group, Precise Positioning and Analytics Department, Fraunhofer Institute for Integrated Circuits IIS, Nordostpark 84, 90411 Nürnberg, Germany;

关键词: 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;   

DOI  :  10.3390/s19051064

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown