Abstract: A system and method for a navigation system (NS) including global navigation satellite system (GNSS) spoofing detection using deep learning, such as a neural network, is provided. A GNSS signal is received by a receiver and one or more metrics are obtained from the received signal. Optionally, one or more non-GNSS signal metrics may also be obtained, e.g., information from an inertial measurement unit, vision system, independent time source, etc. The various metrics are fed into a trained neural network that decides as to whether spoofing is present and, if so, what is the type of spoofing that is occurring.

Abstract: A system and method for utilizing a neural network or other artificial intelligence to identify windows of potential interference in a radio frequency signal is provided. The identified windows are then utilized in a rules-based interference detection system to identify a center, upper, and lower frequencies of the interference. The identified interference may then be remediated using conventional techniques.

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.

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.

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.

Abstract: An architecture and a method are provided for decoding codewords for codes such as low density parity check (LDPC) codes. An iterative decoding algorithm such as the Belief Propagation Algorithm (BPA) is employed that attempts to correct errors in an input block of symbols via a structure containing two sets of nodes through node processing and the passing of messages between nodes. Message passing and node processing is performed in a digit-serial manner instead of a bit-parallel manner.

Abstract: An architecture and a method are provided for decoding codewords for codes such as low density parity check (LDPC) codes. An iterative decoding algorithm such as the Belief Propagation Algorithm (BPA) is employed that attempts to correct errors in an input block of symbols via a structure containing two sets of nodes through node processing and the passing of messages between nodes. Message passing and node processing is performed in a digit-serial manner instead of a bit-parallel manner.