USA As Represented by Secretary of the Navy

John David Reeder

Benjamin J Migliori

Michael W Walton

Diego Marez

A self-supervised machine-learning system identifies whether an intermittent signal is present. The system includes a receiver, an encoding neural network, a decoding neural network, and a gating neural network. The receiver detects radiation and from the detected radiation generates a sampled sequence including sampled values describing the intermittent signal and noise. The encoding neural network is trained to compress each window over the sampled sequence into a respective context vector having a fixed dimension less than an incoming dimension of the window. The decoding neural network is trained to decompress the respective context vector for each window into an interim sequence describing the intermittent signal while suppressing the noise. The gating neural network is trained to produce a confidence sequence from a sigmoidal output based on the interim sequence. Despite the noise, the confidence sequence identifies whether the intermittent signal is present in each sampled value in the sampled sequence.

Neural network approach for identifying a radar signal in the presence of noise

A system is provided for performing a predetermined function within a total area of operation, wherein the system includes a plurality of autonomous agents. Each autonomous agent is able to detect respective local parameters. Each autonomous agent uses a Kalman filter component to establish an environment state based a plurality of state measurements over time. The output of the Kalman filter component within a respective agent is applied to reinforcement learning by an actor-critic task controller, within the respective agent, to determine a subsequent action to be performed by the respective agent in accordance with a reward function. Each agent includes a Kalman consensus filter that addresses errors of the plurality of state measurements over time.

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San Diego, CA, United States of America

John David Reeder