Niitsoo A, Edelhaeusser T, Mutschler C (2018)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2018
Publisher: IEEE
City/Town: NEW YORK
Pages Range: (to appear)
Conference Proceedings Title: 2018 NINTH INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN 2018)
DOI: 10.1109/IPIN.2018.8533766
Object localization and tracking is essential for many applications including logistics and industry. Many local Time-of-Flight (ToF)-based locating systems use synchronized antennas to receive radio signals emitted by mobile tags. They detect the Time-of-Arrival (TOA) of the signal at each antenna and trilaterate the position from the Time Difference-of-Arrival (TDoA) between antennas. However, in multipath scenarios it is difficult to extract the correct ToA. This causes wrong positions.
This paper proposes a signal processing method that uses deep learning to estimate the absolute tag position directly from the raw channel impulse response (CIR) data. We use the CIR together with ground truth positional data to train a convolutional neural network (CNN) that not only estimates non-linearities in the signal propagation space but also analyzes the signal for multipath effects. Our evaluation shows that our position estimation works in multipath environments and also outperforms classical signal processing in line-of-sight situations.
APA:
Niitsoo, A., Edelhaeusser, T., & Mutschler, C. (2018). Convolutional Neural Networks for Position Estimation in TDoA-based Locating Systems. In IEEE (Eds.), 2018 NINTH INTERNATIONAL CONFERENCE ON INDOOR POSITIONING AND INDOOR NAVIGATION (IPIN 2018) (pp. (to appear)). Nantes, FR: NEW YORK: IEEE.
MLA:
Niitsoo, Arne, Thorsten Edelhaeusser, and Christopher Mutschler. "Convolutional Neural Networks for Position Estimation in TDoA-based Locating Systems." Proceedings of the 9th International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes Ed. IEEE, NEW YORK: IEEE, 2018. (to appear).
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