US 11,988,753 B2
GNSS-receiver interference detection using deep learning
Robert Hang, Calgary (CA); and Joyce Chidinma Chidiadi, Calgary (CA)
Assigned to NovAtel Inc., Calgary (CA)
Filed by NovAtel Inc., Calgary (CA)
Filed on Nov. 7, 2022, as Appl. No. 17/982,021.
Application 17/982,021 is a continuation of application No. 16/860,536, filed on Apr. 28, 2020, abandoned.
Prior Publication US 2023/0068027 A1, Mar. 2, 2023
Int. Cl. G06N 3/04 (2023.01); G01S 19/21 (2010.01); G01S 19/37 (2010.01); G06N 3/044 (2023.01); G06N 3/063 (2023.01); G06T 7/00 (2017.01); G06V 10/44 (2022.01)
CPC G01S 19/21 (2013.01) [G01S 19/37 (2013.01); G06N 3/044 (2023.01); G06N 3/063 (2013.01); G06T 7/0002 (2013.01); G06V 10/454 (2022.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. 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.