XL SCIENTIFIC LLC — Department of Defense SBIR Phase I: DTRA212-006

Description

Our proposal addresses the need for an algorithm to support the fusion of multiple and varied detector outputs into an actionable information. Specific focus is to identify and localize a radiation source in a complex environment and to characterize the complete scene. We will apply our extensive experience with radiation detection to thoroughly model a variety of radiation detectors deployed to complex scenes with multiple obstacles and various sources of radiation. Our proposed solution will be: 1)         Robust – we use both statistical theory and recent developments in Robust AI for the US Government customers. Our machine learning methods are already being used with satellite data and applied to military space applications. Robustness of our methods will be thoroughly probed and proved through extensive testing and V&V activities. 2)         Operationally efficient – achieved through detailed realistic modeling and extensive testing of the techniques we have recently developed for USAF customers. 3)         Finely tuned to the specifics of the application – with the detailed realistic modeling of radiation detectors we develop algorithms that are specially designed to address requirements and constraints of the application. Our algorithms will enable quick and efficient decisions protecting the warriors in the field and contributing to the mission success.