SBIR-STTR Award

Advances in automated sensor technologies offer opportunities for enhancing mission performance with individual and teams of Unmanned Air Vehicles, reducing operator workload, advancing multi-vehicle cooperation capabilities, and enhancing situation awareness across the intelligence enterprise. This research will develop operator interfaces for managing and supervising a collection of sensor automation capabilities within dynamic mission contexts. The goal is to enable operators to work collaboratively with the automation technology by allowing users to delegate tasks at various levels of automation, providing visibility into the automation"s reasoning processes, and the allowing the operator to monitor and direct the automation"s"attention"and priorities in real time. We will employ cognitive task analyses to understand the cognitive demands of sensor operators and intelligence analysts, and to understand how automated sensor capabilities can support their performance. In parallel, we will investigate sensor automation technologies to characterize their functionality, competence, and performance vulnerabilities. Operator interfaces will be developed based on a synthesis of the cognitive interview and technology assessment results, in accordance with Joint Cognitive System and Ecological Interface Design principles. These concepts will be vetted with the operational community at the end of Phase I, and then fully-developed in Phase II.

Benefit:
Solutions developed under this effort will have direct application to sensor operators and intelligence analysts, enabling fewer operators to manage larger numbers of vehicles. These solutions could also support the rapidly growing commercial UAV market, making more advanced capabilities available to non-expert users, and offering new and innovative capabilities to commercial UAV developers. Finally, the results of this research can guide the development of new sensor automation technologies that will have high payoff to the military and commercial communities.

Keywords:
ISR, cognitive deman

In current UAV systems, sensor management and the extraction of actionable information from sensor video is largely a manual process that requires near-continuous visual attention. Automating these functions will dramatically increase the overall level of UAV autonomy, thereby paving the way for fewer operators to manage more assets. This effort focuses on the operator interface to sensor automation technologies that will support human-automation collaboration and produce flexible, resilient performance in dynamic missions. Using a Cognitive Systems Engineering approach, an operator interface is being developed for automation technologies that are capable of detecting and reporting events, and performing robust target tracking using target re-identification capabilities. This interface includes features for managing attention and situation awareness, managing shifting task priorities, and transitioning among different tasks in a multi-UAV control context.A fully capable prototype of this user interface will be developed and packaged into a software plug-in that can be interfaced with any control station that possesses its own API.

Benefit:
Solutions developed under this effort will have direct application to sensor operators and intelligence analysts, enabling fewer operators to manage larger numbers of vehicles.These solutions could also support the rapidly growing commercial UAV market, making more advanced capabilities available to non-expert users, and offering new and innovative capabilities to commercial UAV developers.Finally, the results of this research can guide the development of new sensor automation technologies that will have high payoff to the military and commercial communities.