Near-field localization using machine learning: An empirical study
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A4 Artikkeli konferenssijulkaisussa
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2021-04
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en
Pages
5
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2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings, IEEE Vehicular Technology Conference, Volume 2021-April
Abstract
Estimation methods for passive near-field localization have been studied to an appreciable extent in signal processing research. Such localization methods find use in various applications, for instance in medical imaging. However, methods based on the standard near-field signal model can be inaccurate in real-world applications, due to deficiencies of the model itself and hardware imperfections. It is expected that deep neural network (DNN) based estimation methods trained on the nonideal sensor array signals could outperform the model-driven alternatives. In this work, a DNN based estimator is trained and validated on a set of real world measured data. The series of measurements was conducted with an inexpensive custom built multichannel software-defined radio (SDR) receiver, which makes the nonidealities more prominent. The results show that a DNN based localization estimator clearly outperforms the compared model-driven method.Description
Publisher Copyright: © 2021 IEEE.
Keywords
deep learning, near-field localization, SDR
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Citation
Laakso, M & Wichman, R 2021, Near-field localization using machine learning: An empirical study . in 2021 IEEE 93rd Vehicular Technology Conference, VTC 2021-Spring - Proceedings ., 9449002, IEEE Vehicular Technology Conference, vol. 2021-April, IEEE, IEEE Vehicular Technology Conference, Helsinki, Finland, 25/04/2021 . https://doi.org/10.1109/VTC2021-Spring51267.2021.9449002