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.
Highlights
Radio-based real-time locating systems (RLTSs) are key to drive automation and digitalization in many applications in warehouse management, production, and manufacturing
RF-based positioning systems may be implemented using a multitude of different technologies, which include angle of arrival (AoA), received signal strength (RSS), time of arrival (ToA), and time difference of arrival (TDoA)
This article presents a position estimation based on deep learning methods that directly operates on the channel impulse responses of TDoA-based locating systems
Summary
Radio-based real-time locating systems (RLTSs) are key to drive automation and digitalization in many applications in warehouse management, production, and manufacturing. While RSS- and AoA-based localization usually come at a low cost and lower accuracy, T(D)oA-based systems require synchronization schemes and have a more complex system setup, which usually makes them more expensive Their better positioning accuracy makes them still attractive for many use-cases, including the tracking of goods in warehouses or virtual and augmented reality in various applications [2,3,4]. This article builds on recent successes of deep learning (DL) [19] in order to estimate positions from raw channel impulse response data in a TDoA-based system setup and extends the work from [1].
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