Abstract: A cognitive radio device may include a radio frequency (RF) detector, an RF transmitter having a selectable hopping frequency window, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal, determine a jammer type associated with the jammer signal from among a plurality of different jammer types based upon a game theoretic model, and operate the RF transmitter to change the selectable hopping frequency window responsive to the determined jammer type of the jammer signal.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to train a plurality of machine learning networks with DAS data from the ?-OTDR based upon different respective optimizers, select a trained machine learning network from among the plurality thereof based upon a game theoretic model, and generate an acoustic event report from the DAS data using the selected trained machine learning network.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to generate a series of covariance matrices for DAS data from the ?-OTDR, and determine an acoustic event based upon comparing the series of covariance matrices with a corresponding Toeplitz matrix.

Abstract: A distributed acoustic sensing (DAS) system may include an optical fiber, a phase-sensitive OTDR (?-OTDR) coupled to the optical fiber, and a processor cooperating with the ?-OTDR. The processor may be configured to generate a series of covariance matrices for DAS data from the ?-OTDR, determine acoustic events based upon the covariance matrices and a machine learning network, and generate an acoustic event report from the acoustic events.

Abstract: An object detection device may include a variational autoencoder (VAE) configured to encode image data to generate a latent vector, and decode the latent vector to generate new image data. The object detection device may also include a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and process the new image data using the selected deep learning model for object detection.

Abstract: A radio frequency (RF) signal classification device may include an RF receiver configured to receive RF signals, a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and process the RF signals using the selected deep learning model for RF signal classification.

Abstract: A cognitive radio device may include an RF receiver configured to receive interfering RF signals, an RF transmitter configured to be selectively operated, a quantum computing circuit configured to perform quantum subset summing, and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and process the received interfering RF signals using the selected deep learning model for selectively operating the RF transmitter.

Abstract: A cognitive radio system may include cognitive radio frequency (RF) radios and a controller configured to selectively change at least one operating parameter of the cognitive RF radios based upon a cognitive group hierarchy. The cognitive group hierarchy may include a first group based upon a signal modulation classification, a second group based upon a waveform requirement, a third group based upon an optimal cognitive RF radio path, a fourth group based upon a cognitive RF radio dynamic spectrum allocation, and a fifth group based upon frequency hopping.

Abstract: A cognitive radio device may include a radio frequency (RF) detector, an RF transmitter having a selectable hopping frequency window, and a controller. The controller may be configured to cooperate with the RF detector and RF transmitter to detect a jammer signal, determine a jammer type associated with the jammer signal from among a plurality of different jammer types based upon a game theoretic model, and operate the RF transmitter to change the selectable hopping frequency window responsive to the determined jammer type of the jammer signal.

Abstract: A perturbation radio frequency (RF) signal generator is provided which generates a perturbed RF output signal to cause a signal classification change by an RF signal classifier. The perturbation RF signal generator may include a quantum computing circuit configured to perform quantum subset summing; and a processor. The processor may be configured to generate a game theory reward matrix for a plurality of different deep learning signal perturbation models, cooperate with the quantum computing circuit to perform quantum subset summing of the game theory reward matrix, select a deep learning signal perturbation model from the plurality thereof based upon the quantum subset summing of the game theory reward matrix, and generate the perturbed RF output signal based upon the selected deep learning signal perturbation model to cause the signal classification change in the RF signal classifier.