Raytheon Company

Jacob M Miller

Shawn T Henderson

H Brown Cribbs, Iii

Joshua A Noble

A method includes obtaining pairs of in-phase (I) and quadrature (Q) samples associated with a signal to be demodulated. The method also includes providing a set of the I/Q pairs to a trained AI/ML model. The set of the I/Q pairs includes an I/Q pair associated with a symbol being demodulated and at least one I/Q pair associated with at least one prior symbol that has been demodulated. In addition, the method includes using the trained AI/ML model to generate a symbol estimate for the symbol based on the set of the I/Q pairs, where the symbol estimate represents a portion of data that is encoded in the signal.

Demodulation of non-return to zero (NRZ) or other signals using multiple-input artificial intelligence/machine learning (AI/ML) model

A method includes using a first trained artificial intelligence/machine learning (AI/ML) model to identify occupied frequency channels associated with one or more incoming signals during multiple time periods, where the one or more incoming signals include a frequency-hopping spread spectrum (FHSS) signal. The method also includes generating one or more baseband signals using portions of the one or more incoming signals associated with the occupied frequency channels. The method further includes using a second trained AI/ML model to recover data from the one or more baseband signals, where the recovered data represents at least a portion of data that is encoded in the FHSS signal.

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State College, PA, United States of America

Jacob M Miller