Sensor Data Fusion (SDF)
Technology developed in this project utilizes Chemical, Biological, Radiological, Nuclear (CBRN), and meteorological sensor readings along with transport and dispersion models to characterize unknown CBRN source properties and refine CBRN downwind hazard assessments.
The Sensor Data Fusion (SDF) project is developing tailored meteorological decision-support applications for the military and domestic emergency-response communities. In particular, these applications are used to enhance DoD's Chemical, Biological, Radiological, and Nuclear (CBRN) hazard prediction toolsets such as the Hazard Prediction and Assessment Capability (HPAC) and more recently the Joint Effects Model (JEM).
A main goal is developing an operational algorithm that can estimate an unknown CBRN source and predict a refined downwind hazard from that source while using available CBRN and meteorological sensor observations. Integrating this algorithm into the HPAC/JEM hazard-prediction toolsets will also be addressed.
To support testing and evaluation of this product, RAL is also developing a virtual testing and evaluation environment, known as Virtual THreat-Response Emulation and Analysis Testbed (VTHREAT). This will provide the capability of simulating a realistic CBRN release scenario, placement of CBRN and meteorological sensors, and extraction of the resulting synthetic sensor readings. These synthetic observations can then be used by the algorithms to evaluate their ability to recreate the CBRN event.
SDF Algorithm demonstation using VTHREAT to produce a release scenario. SCIPUFF demonstrates source characterization and hazard refinement. Play animation