Cognitive Aerosapce Trusted Edge Sensing

This program will aim to address the size, weight, power and cost challenges associated sensor data processing and analysis at the edge that today limit platform capabilities in Low Earth Orbit (LEO) as well as other aerospace and airborne settings. Existing platforms are limited by storage and downlink capabilities, power budget, and delayed availability, data assurance, sensor node resiliency as well as adversarial threats. Aerospace platforms need to reliably perform on-board machine learning and signal processing while operating within a constrained size, weight and power envelope in order to sustain the surge in demand for analytics. Moving more processing power on board, adjacent to sensing payloads, reduces the pressure on available communication bandwidth. This, in turn, supports scaling to larger satellite deployments which ultimately increases the reliability of the aerospace analytics platforms. Aerospace platforms with the flexibility and efficiency to execute state-of-the-art machine learning algorithms in-flight will enable the deployment of autonomous, adaptable and resilient assets that can be re-tasked as mission requirements evolve. This cognitive processing combined with sensor innovations will enable new mission capabilities. Satellites will be able to act as cognitive distributed sensor networks. Sensors across several satellites will be able to concurrently track different properties of a set of targets (such as missiles) and provide the relevant analytics in real-time – this would be a case of distributed analytics based on multi-modal sensor data. In applications involving sensor networks, node communications can become limited by severe capacity, energy or active adversarial constraints. The distributed sensor networks provide an application setting in which distributed optimization tasks (including machine learning) can address the aforementioned challenges through distributed training and prediction.