Sept. 17 (UPI) -- The United States and Britain have announced a jointly funded project to rapidly and automatically process data obtained from sensors and optimize that information for military use.
The project is led by the U.S. Army Combat Capabilities Development and Command Army Research Laboratory, CCDC-Atlantic and the U.K. Defense Science and Technology Laboratory, according to the Army.
Professor Simon Godsill of the University of Cambridge will lead the project based on his proposal, "SIGNets -- Signal and Information Gathering for Networked Surveillance."
Godsill's proposal was one of several submitted for the project and reviewed by experts from both governments, and he will work with Professors Wenwu Wang and Pei Xiao from the University of Surrey and Professor Lyudmila Mihaylova from the University of Sheffield on the project.
Godsill won a three-year, $1.2 million grant and will begin research at the end of September.
"We were impressed with the overall quantity and quality of the submitted proposals. The winning proposal has an outstanding multi-disciplinary research team with highly innovative and integrated technical approaches," said Dr. Tien Pham, U.S. project lead from CCDC ARL.
The research will focus on sensor signaling in complex environments, examining three problems: how to manage task and resource allocation for autonomous sensors, how to maintain computational effectiveness of the network of sensors, and how to characterize and quantify uncertainties in sensor-derived estimates.
"Emerging technologies such as cheap, lightweight uncrewed aerial vehicles provoke a need for research into information processing of data derived from multiple autonomous sensors," said Alasdair Hunter, the Britain's lead for DSTL.
In the military context, sensors have to work in a potentially contested environment, so networks of sensors are required to be resilient against attack and failure of individual sensors and communication links. This project addresses the challenges arising from the design of resilient networks by developing novel, fundamental information processing algorithms."