Multi-Sensor/Multi-Platform Integration and Sensor Resource Management

Today’s Air Force environment is moving toward unmanned air vehicles (UAVs) that are smaller and have requirements for operating in and near hostile A2/AD environments. Autonomous systems provide a considerable opportunity to reduce costs, enhance capabilities, and increase levels of operational reliability, persistence and resilience. Increased levels of autonomy can benefit manned and unmanned systems, in integrated air and space domains, and across the command and control, intelligence, surveillance and reconnaissance applications. As the size of the individual UAVs shrinks, there is a corresponding limitation on the available payloads which in turn results in a requirement for multiple sensors ranging from electro-optic/infrared, radio frequency direction finding, electronic support, electronic attack, and long range communications to be shared across the swarm. This sharing of resources forces requirements of distributed and collaborative sensor and resource management capability to achieve mission goals. The focus of our Phase II effort is the continued development of an autonomous resource manager that collaborates across a distributed heterogeneous swarm of UAV sensors. We will investigate the use of deep learning techniques to solve this problem with minimal human interactions. This effort will conclude with a field test demonstration