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US 2023/0068027 A1Published application
Gnss-receiver interference detection using deep learning
AssigneeNovAtel Inc.
InventorsRobert Hang, Joyce Chidinma Chidiadi
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Abstract
Systems and methods are described for classification of interference for GNSS receivers. One or more neural networks are utilized to classify RF signal data received by a GNSS receiver. The classification associates the RF signal data with an RF environment. Appropriate interference mitigation techniques can be implemented by the receiver based on the classification.
Claims20
A Global Navigation Satellite System (GNSS) processing architecture, the GNSS processing architecture comprising:
- a processor;
- and
- a memory unit in communication with the processor via a communication infrastructure and configured to store processor-readable instructions;
- wherein, when executed by the processor, the processor-readable instructions cause the processor to:
- receive signal data including one or more input metric values for one or more types of input parameters;
- provide the signal data to a neural network for classification of the signal data;
- and
- classify, using the neural network, the signal data as being of a particular type of interference environment of a plurality of different types of interference environments.
5 more elements
A method comprising:
- receiving, at a Global Navigation Satellite System (GNSS) processing architecture, signal data including one or more input metric values for one or more types of input parameters of a plurality of different input parameters,
- wherein the signal data is from one or more received GNSS signals;
- providing the signal data to a neural network for classification of the signal data;
- and
- classifying, using the neural network, the signal data as being of a particular type of interference environment of a plurality of different types of interference environments.
2 more elements
A Global Navigation Satellite System (GNSS) receiver configured to:
- receive signal data including one or more input metric values for one or more types of input parameters of a plurality of different types of input parameters, wherein the signal data is from one or more GNSS signals;
- provide the signal data to a neural network for classification of the signal data;
- and
- classify, using the neural network, the signal data as being of a particular type of environment of a plurality of different types of environments,
- wherein the classification is used to identify a mitigation technique of a plurality of different mitigation techniques.
2 more elements
Dependencies are inferred from the claim text.