The Armyâs future vision is to have a âhigh-fidelity replication/representation of real combat to include direct and indirect fire weapons and non-lethal fires. Also, to conduct live training in conjunction with immersive (live-mixed reality and constructive) training enabling units, all over the world, to train together, improving multi-echelon training exercises. Most importantly to be able to conduct tough, iterative, dynamic, and realistic multi-echelon combined arms maneuver and mission command training in support of Multi-Domain Operations.â - from Synthetic Training Environment (STE) Live Training Environment (LTE) Statement of Need (SoN). Being able to use immersive live-mixed reality, such as Augmented Reality (AR) Head-Mounted Displays (HMD) at the Point of Need, is essential to implementing the Armyâs vision. AR HMDs provide deployed soldiers with a more immersive training capability, such as realistic representations of real combat environments where soldiers can visualize direct and indirect fired weapons munitions, non-lethal fire effects, and much more. However, there are several challenges, such as bandwidth and latency, to implementing AR HMDs in Live Training immersive live-mixed reality environments across Home Stations (HSs), Combat Training Centers (CTCs), and deployed locations. To solve the bandwidth and latency challenges, TITENN is proposing to develop an Autonomous Adaptive Network (AAN). AAN is a game-changing technology for the Army and will address core challenges with using AR HMDs to support STE LTEâs vision, which operates in a decoupled cloud simulation environment supporting real-time AI processing. AAN enables true replication/representation of real combat to include Direct and Indirect Fire weapons, non-lethal fires, as well as culturally-specific human behaviors, autonomous civilians, and military equipment behaviors (robots, drones, droids, etc.). AAN also facilitates offloading computationally expensive processing into the cloud. AANâs low latency concept also advances current security models enabling a high-performance synthetic environment to meet complex requirements for training and integration of STE LTEâ
