The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 813–820, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-813-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 813–820, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-813-2020

  21 Aug 2020

21 Aug 2020

A SEQUENCE-TO-SEQUENCE TEMPORAL CONVOLUTIONAL NEURAL NETWORK FOR IONOSPHERE PREDICTION USING GNSS OBSERVATIONS

M. Kaselimi, N. Doulamis, A. Doulamis, and D. Delikaraoglou M. Kaselimi et al.
  • School of Rural and Surveying Engineering, National Technical University of Athens, Athens 15780, Greece

Keywords: ionosphere variability, GNSS signal, convolutional neural networks, precise point positioning, total electron content

Abstract. This paper proposes a model suitable for predicting the ionosphere delay at different locations of receiver stations using a temporal 1D convolutional neural network (CNN) model. CNN model can optimally addresses non-linearity and model complex data through the creation of powerful representations at hierarchical levels of abstraction. To be able to predict ionosphere values for each visible satellite at a given station, sequence-to-sequence (seq2seq) models are introduced. These models are commonly used for solving sequential problems. In seq2seq models, a sequential input is entered to the model and the output has also a sequential form. Adopting this structure help us to predict ionosphere values for all satellites in view at every epoch. As experimental data, we used global navigation satellite system (GNSS) observations from selected sites in central Europe, of the global international GNSS network (IGS). The data used are part of the multi GNSS experiment (MGEX) project, that provides observations from multiple navigation satellite systems. After processing with precise point positioning (PPP) technique as implemented with GAMP software, the slant total electron content data (STEC) were obtained. The proposed CNN uses as input the ionosphere pierce points (IPP) points coordinates per visible satellite. Then, based on outcomes of the ionosphere parameters, the temporal CNN is deployed to predict future TEC variations.