Machine learning in GNSS receivers for improved velocity outputs
US12306312B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 8, 2022 |
| Grant date | May 20, 2025 |
| Priority date | — |
| Expiry date | Aug 20, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG01S19/42
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Machine learning techniques are used to compute predicted range rate errors in a GNSS receiver. In one embodiment, training data is computed to provide true range rate error data for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the true range rate error data to train a model (e.g., a set of one or more neural networks) that can produce predicted range rate errors for use in correcting measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct Doppler measurements using the predicted range rate errors provided by the trained set of one or more neural networks.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.