Machine learning assisted satellite based positioning

A device, comprising:

• at least one processor;
• and
• a memory including instructions that, when executed by the at least one processor, cause the at least one processor to:
• receive an estimated position based on a positioning system comprising a Global Navigation Satellite System (GNSS) satellite;
• receive a set of parameters associated with the estimated position;
• apply the set of parameters and the estimated position to a machine learning model, the machine learning model having been trained based at least on a position of a receiving device relative to the GNSS satellite;
• provide the estimated position and an output of the machine learning model to a Kalman filter;
• and
• provide an estimated device location based on an output of the Kalman filter.

A computer program product comprising code stored in a tangible computer-readable storage medium, the code comprising:

• code to receive an estimated position of a first device based on a positioning system comprising a Global Navigation Satellite System (GNSS) satellite;
• code to receive a set of parameters associated with the estimated position, the set of parameters comprising at least one parameter corresponding to a position of the first device relative to the GNSS satellite;
• code to receive a reference position of the first device based on a reference positioning system;
• code to generate a machine learning model based on the estimated position, the reference position and the set of parameters;
• and
• code to provide the machine learning model to a second device, to estimate device location of the second device based on a subsequent position of the second device as provided by the positioning system.