Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction

@inproceedings{Piccolomini2019RecurrentNN,
  title={Recurrent Neural Networks Applied to GNSS Time Series for Denoising and Prediction},
  author={E. Loli Piccolomini and Stefano Gandolfi and Luca Poluzzi and Luca Tavasci and Pasquale Cascarano and Andrea Pascucci},
  booktitle={TIME},
  year={2019}
}
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… CONTINUE READING