Unified Operational Picture for Anti-Submarine Warfare

During Phase I, ThayerMahan was able to build, test, and validate on relevant internal data sets an integrated, multi-layered platform for detecting, tracking, and correlating multi-modal data to deliver an integrated track for each vessel in a given operating area.  The work we propose for Phase II moves our Phase I-validated architecture and modules, which were trained and proven on prototype-scale training data, to a fully operational system trained and optimized on vastly larger training data and more diverse scenarios.  The key technical/data gap in Phase I was active acoustic data, which will be our top priority focus during the the Phase II Base.  The proposed work will lead to the delivery of a state-of-the-art, automated system which delivers robust tracking and a Unified Operational Picture (UOP) for Anti-Submarine Warfare (ASW) which operates vastly faster and with higher accuracy than current systems and workflows can provide, while retaining the full value of spectral and kinematic data collected by sensors.  The expanded solution positions the Navy to rapidly declutter the tactical ASW picture, target threats faster, and operate at a higher level of Situational Awareness for C2 and fires.     We will also deliver on the additional IWS-5 objectives outlined to our team, e.g., a product which can detect an acoustic energy signal, track it across bearings and through time, and re-identify it on the same or another sensor at a future point in time.  Ongoing internal research and development at ThayerMahan since the end of Phase I shows continued improvement in auto-scissoring and target featurization capabilities, each of which will deliver enhanced functionality to these IWS-5 priority areas.  During the course of Phase II, we will evolve this capability into a fully operational system which performs this entire task automatically in concert with the UOP for ASW capability outlined in the original SBIR.    Our work is divided into three key areas: a) Optimization of neural networks and AI systems – including extending our proven architecture to encompass active sonar returns and outputs from Navy classification systems; b) Data pipeline engineering and neural network retraining – this work focuses on delivering end-to-end data flows which ensure robust operational performance, and extending data sets to retrain algorithms and improve overall performance, and; c) Software engineering and integration – this effort focuses on the display of our Unified Operational Picture, first using ThayerMahan tools, and then integrating with Navy interface systems, as well as the operationalization of our platform and its containerization and integration with Navy systems.