Systems and methods for predicting ionospheric electron content
AssigneeCACI, Inc.—Federal
InventorIsaac P. Moorman
Life of this patent
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Abstract
A system may be configured to predict total electron content (TEC) in an ionosphere. Some embodiments may: provide a machine learning (ML) model; obtain a dataset; input the dataset into the ML model; predict, for a predetermined number of days, the TEC using the ML model; and observe a performance improvement over the obtained dataset based on the prediction, the prediction being made for a region having a number of ground transmitters satisfying a sparseness criterion.
Claims20
A method for predicting total electron content (TEC) in an ionosphere, comprising:
- providing a machine learning (ML) model;
- obtaining a dataset;
- inputting the dataset into the ML model;
- predicting, for a predetermined number of days, the TEC using the ML model;
- and
- observing a performance improvement over the obtained dataset based on the prediction,
- wherein the prediction is made for a region having a number of ground transmitters satisfying a sparseness criterion.
A method for predicting TEC in an ionosphere, comprising:
- providing an LSTM-GAN ML model in a combination of neural networks;
- obtaining a dataset;
- inputting the dataset into the LSTM-GAN ML model;
- predicting, for a predetermined number of days, the TEC using the LSTM-GAN ML model;
- and
- determining a delay of a signal based on the prediction.
A method for predicting TEC in an ionosphere, comprising:
- providing an LSTM-GAN ML model in a combination of neural networks;
- obtaining a dataset;
- inputting the dataset into the LSTM-GAN ML model;
- predicting, for a predetermined number of days, the TEC using the LSTM-GAN ML model;
- and
- determining, for geolocating a transmitter, a location of the signal exiting the ionosphere based on the prediction.
Dependencies are inferred from the claim text.