USA as Represented by Secretary of the Navy

Randall S Plate

Cherry Y Wakayama

Douglas John Grimmett

Chad Williams

Carrick Talmadge

At a source node, a plurality of packets may be determined for transmission to a destination node in a network comprising a plurality of network nodes. A transmission rate of the plurality of packets from the source node to a neighbor node in the network may be adaptively controlled, based on a determination of a current status of the network by utilizing a plurality of parameters that are estimated via a reinforcement learning routing algorithm. The plurality of parameters include an estimated cost value representing a current cost to transmit the plurality of packets to the destination node via the network. Transmissions from intermediate nodes may also be adaptively deferred based on a determination of a current status of the network by utilizing the plurality of parameters.

Reinforcement learning-based intelligent control of packet transmissions within ad-hoc networks

Systems and methods for maritime infrasound detection can include at least one waterborne platform. A microbarometer, inertial measurement unit (IMU) and heave cancellation component (HCC) can be mounted on the waterborne platform. The HCC can receive a pressure input from the microbarometer and the IMU to generate an output pressure that can be indicative of an infrasound signal of interest (SOI). The HCC can further include a filter and an adaptive algorithm. The filter can receive IMU pressure and environment noise pressure as inputs. An error signal from the HCC output can also be supplied through a closed feedback loop that includes a Recursive Least Squares adaptive algorithm, which can further include a weighted, tapped delay line. The system can further include a remote data center for receiving the HCC output from the waterborne platform(s), which can be buoys, vessel or USV's.

Motion compensating infrasound sensor

A reinforcement learning-based method is provided that enables efficient communication for networks having varying numbers and topologies of mobile and stationary nodes. The method provides an autonomous, optimized, routing method that may be implemented in a distributed manner among the nodes that allows the nodes to make intelligent decisions of how to forward data from a source node to a destination node with little or no a priori information about the network. The method involves receiving, at a node within a distributed network, data packets containing position and velocity information from a transmitting node. Position and velocity estimates are determined for the transmitting and receiving nodes using the position and velocity information. State-action pair value estimates are determined in the destination direction for forward packets and the source direction for backward sweeping packets, along with associated destination direction and source direction state value estimates, which determine packet transmittal.

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San Diego, CA, United States of America

Randall S Plate