Machine-learning augmented synthetic aperture sonar

A system comprising:

• a plurality of autonomous underwater devices configured to operate cooperatively as a swarm, the plurality of autonomous underwater devices comprising:
• a first autonomous underwater device comprising:
• a propulsion system configured to propel the first autonomous underwater device within an aquatic environment;
• an on-board navigation control system coupled to the propulsion system and configured to send navigation control signals to the propulsion system to move the first autonomous underwater device based on swarm operation data to cause the first autonomous underwater device to follow a course that limits sonar system interference between devices of the swarm;
• one or more receiver arrays, each receiver array comprising a plurality of receiver elements, each receiver element configured to generate a signal responsive to detecting sound energy in the aquatic environment;
• and
• one or more processors coupled to the one or more receiver arrays and configured to receive signals from the receiver elements, to generate input data based on the received signals, and to provide the input data to a machine learning model associated with the swarm to generate model output data, wherein the model output data includes sonar image data based on the sound energy, a label associated with the sonar image data, or both;
• and
• a second autonomous underwater device comprising one or more sonar transducers configured to emit one or more pings into the aquatic environment.

A method comprising:

• emitting, from a first autonomous underwater device, sound energy in an aquatic environment via one or more pings;
• receiving, at the first autonomous underwater device from a second autonomous underwater device, an electromagnetic waveform encoding a communication signal;
• based on the communication signal, determining synthetic aperture sonar settings data to limit interferences between synthetic aperture sonar systems of devices of a swarm of autonomous underwater devices;
• detecting, at the first autonomous underwater device, sonar returns based on the one or more pings;
• generating input data based on the sonar returns and the communication signal;
• and
• providing the input data to a machine learning model on-board the first autonomous underwater device to generate model output data, wherein the model output data includes sonar image data based on the sonar returns, a label associated with the sonar image data, or both.

A method comprising:

• obtaining position data indicating locations of one or more underwater devices of a swarm of devices in an aquatic environment;
• determining navigation data for at least one device of the swarm of devices based on output of a tactical machine learning model that is trained to predict adversarial actions during an engagement between an adversarial agent and one or more friendly agents, to select responses to detected adversarial actions, or both;
• and
• sending the navigation data to a navigation control system onboard the at least one device to cause the at least one device to, in cooperation with one or more other devices of the swarm of devices, gather synthetic aperture sonar data in a particular area by selecting one or more synthetic aperture sonar settings to limit interferences between synthetic aperture sonar systems of devices of the swarm of devices.