Motorola Solutions, Inc.

Vaxinnate Corporation

David N Taylor

Gregory M Agami

Thomas J Powell

Nathanael P Kuehner

Langzhou Song

Lynda G Tussey

Yi Zhang

Rodger W Caruthers

Alan R Shaw

Scott W Umlauf

Ge Liu

Robert S Becker

Stephen J Govea

Micah D Silberstein

Systems and methods for classifying radio frequency signal modulations include receiving, at a consolidated neural network, a complex quadrature vector of interest representative of a baseband signal derived from a radio frequency signal, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving, from the consolidated neural network, a classification result for the baseband signal. The consolidated neural network may be trained to classify baseband signals with respect to known modulation types by receiving complex quadrature training vectors, each including samples of a baseband signal derived from a radio frequency signal of known modulation type, comparing a classification result for the training vector to the known modulation type to determine modulation classification performance, and modifying a configuration parameter of the consolidated neural network dependent on the determined modulation classification performance.

Systems and methods for modulation classification of baseband signals using multiple data representations of signal samples

Systems and methods for classifying baseband signals with respect to modulation type include receiving, at a consolidated neural network whose objective is modulation classification performance, a complex quadrature vector of interest including multiple samples of a baseband signal derived from a radio frequency signal of unknown modulation type, generating multiple data representations of the vector of interest, providing each data representation to one of multiple parallel neural networks in the consolidated neural network, and receiving a classification result for the baseband signal based on combined outputs of the parallel neural networks. The consolidated neural network may be trained to classify baseband signals with respect to known modulation types by receiving complex quadrature training vectors, each including samples of a baseband signal derived from a radio frequency signal of known modulation type, and comparing a classification result for the training vector to the known modulation type to determine modulation classification performance.

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West Dundee, IL, United States of America

David N Taylor