Apple Inc.

Atheros Communications, Inc.

Qualcomm Atheros Inc.

Dennis Hilgenberg

Brent Michael Ledvina

Yi-Hsiu Wang

Benjamin A Werner

Aarti Sathyanarayana

A device implementing a system for estimating device location includes at least one processor configured to receive an estimated position based on a positioning system comprising a Global Navigation Satellite System (GNSS) satellite, and receive a set of parameters associated with the estimated position. The processor is further configured to 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. The processor is further configured to 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.

Machine learning assisted satellite based positioning

A method for determining portions of a GPS satellite signal may use non-coherent integration to determine a repeated pattern such as a preamble. Once the repeated pattern is determined, portions of the GPS satellite signal that may be determined with partial correlation sums. Sensitivity to satellite signals may be increased by computing more partial correlation sums on portions of the GPS satellite signal. In one embodiment, time of day information may be determined from the GPS satellite signal with partial correlation sums.

High sensitivity GPS-assisted time source

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San Jose, CA, United States of America

Dennis Hilgenberg