Search Results

Documents authored by Poluzzi, Luca

Document
DOI: 10.4230/LIPIcs.TIME.2019.10
Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction

Authors: Elena Loli Piccolomini, Stefano Gandolfi, Luca Poluzzi, Luca Tavasci, Pasquale Cascarano, and Andrea Pascucci

Published in: LIPIcs, Volume 147, 26th International Symposium on Temporal Representation and Reasoning (TIME 2019)

Abstract
Global Navigation Satellite Systems (GNSS) are systems that continuously acquire data and provide position time series. Many monitoring applications are based on GNSS data and their efficiency depends on the capability in the time series analysis to characterize the signal content and/or to predict incoming coordinates. In this work we propose a suitable Network Architecture, based on Long Short Term Memory Recurrent Neural Networks, to solve two main tasks in GNSS time series analysis: denoising and prediction. We carry out an analysis on a synthetic time series, then we inspect two real different case studies and evaluate the results. We develop a non-deep network that removes almost the 50% of scattering from real GNSS time series and achieves a coordinate prediction with 1.1 millimeters of Mean Squared Error.
Cite as

Elena Loli Piccolomini, Stefano Gandolfi, Luca Poluzzi, Luca Tavasci, Pasquale Cascarano, and Andrea Pascucci. Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction. In 26th International Symposium on Temporal Representation and Reasoning (TIME 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 147, pp. 10:1-10:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

Copy BibTex To Clipboard

@InProceedings{lolipiccolomini_et_al:LIPIcs.TIME.2019.10,
  author =	{Loli Piccolomini, Elena and Gandolfi, Stefano and Poluzzi, Luca and Tavasci, Luca and Cascarano, Pasquale and Pascucci, Andrea},
  title =	{{Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction}},
  booktitle =	{26th International Symposium on Temporal Representation and Reasoning (TIME 2019)},
  pages =	{10:1--10:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-127-6},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{147},
  editor =	{Gamper, Johann and Pinchinat, Sophie and Sciavicco, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2019.10},
  URN =		{urn:nbn:de:0030-drops-113687},
  doi =		{10.4230/LIPIcs.TIME.2019.10},
  annote =	{Keywords: Deep Neural Networks, Recurrent Neural Networks, Time Series Denoising, Time Series Prediction}
}
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact