Abstract: First global navigation satellite systems (GNSS) signal data is obtained from a set of GNSS satellites. First satellite state data comprising estimated orbit data valid at one or more particular times for the set of GNSS satellites is obtained. A non-linear satellite state model is generated by adjusting a plurality of model parameters using one or more machine learning techniques based on the first GNSS signal data and the first satellite state data. The non-linear satellite state model generates output satellite state data based on GNSS signal data. GNSS satellite state analysis instructions are generated that are configured to allow a second computer system to access the non-linear satellite state model to generate updated satellite state data for one or more particular GNSS satellites by applying the non-linear satellite state model to particular GNSS signal data corresponding to the one or more particular GNSS satellites.

Abstract: Techniques are provided for non-linear satellite state modeling. First global navigation satellite systems (GNSS) signal data is obtained from a set of GNSS satellites. First satellite state data is obtained. The first satellite state data includes orbit data for the set of GNSS satellites. A non-linear satellite state model that includes a plurality of model parameters is generated. The non-linear satellite state model is generated by adjusting the plurality of model parameters based on the first GNSS signal data and the first satellite state data. The non-linear satellite state model outputs satellite state data based on GNSS signal data. Second GNSS signal data is obtained from the set of GNSS satellites. A set of updated satellite state data is calculated using the non-linear satellite state model and the second GNSS signal data.

Abstract: Techniques are provided for non-linear satellite state modeling. First global navigation satellite systems (GNSS) signal data is obtained from a set of GNSS satellites. First satellite state data is obtained. The first satellite state data includes orbit data for the set of GNSS satellites. A non-linear satellite state model that includes a plurality of model parameters is generated. The non-linear satellite state model is generated by adjusting the plurality of model parameters based on the first GNSS signal data and the first satellite state data. The non-linear satellite state model outputs satellite state data based on GNSS signal data. Second GNSS signal data is obtained from the set of GNSS satellites. A set of updated satellite state data is calculated using the non-linear satellite state model and the second GNSS signal data.

Abstract: A neural network is used to process raw, uncorrected signals received by a Global Positioning System (GPS) receiver to obtain signal corrections which are used to correct the raw signals and obtain highly accurate position coordinate data. The neural network is trained with a particular GPS receiver. Once the neural network is trained, the weight matrices used for the particular GPS receiver may be used in different GPS receivers without requiring training of the different GPS receivers.