APL, in collaboration with the Naval Air Warfare Center Port Hueneme Weapons Division, led a swarming unmanned surface vehicle demonstration of advanced multivehicle autonomy at tactically relevant speeds. We developed a “plug and play” kit that converts any Navy high-speed maneuverable surface boat into an autonomous, unmanned vehicle—as well as the autonomy control segments to enable a six-vehicle, open-water demonstration at speeds faster than 40 knots. The demonstration included tactical situations such as bad-actor engagement and coordinated attack scenarios—illustrating a potential leap-ahead capability the Navy can use to address a range of operational challenges.
Submarine Survivability Program
The SSN/SSGN survivability program ensures our submarines stay hidden regardless of new technology, changing mission requirements, and increasingly sophisticated adversaries. We take a fundamental physics approach to understanding submarine detection and counterdetection by developing detailed models validated with full-scale sea tests. This year's studies, analyses, and sea tests improved our understanding of the physics of submarine stealth, allowing submarine operators to develop better tactical guidance and allowing the resourcing and acquisition communities to establish requirements—and ultimately to shape designs of the next-generation fast-attack submarine.
Integrated Undersea Surveillance Systems
We are solving critical challenges for the Navy’s undersea surveillance community, making contributions such as active and passive sonar processing algorithms for inclusion in the Advanced Surveillance Build tactical sonar modernization program. We provided direction for both U.S. and allied platforms to complete initial operational testing and evaluation for the Surveillance Towed Array Sensor System (SURTASS), and developed modular, quick-reaction, mobile surveillance system capabilities to extend SURTASS presence worldwide. Our laboratory-based fleet operator watch section testing characterized new tactical sonar functionality and capabilities, and included developmental concepts of employment.
Unmanned Surface Vessel Perception
International regulations for preventing collisions at sea require vessels to operate within certain distances based on the visual identification of other vessels. We have applied machine-learning techniques to teach autonomous vessel-piloting systems how to identify and then safely respond to other ships. Our team compiled an extensive database of images at diverse aspects, ranges, and environmental conditions, and used this data to design and demonstrate a vessel-recognition system accurate enough to meet international requirements for safe passage of vessels.