Multi-Sensor Data Fusion Frameworks for Layered Sensing

ABSTRACT: The objective of this effort is to develop a framework to integrate data from various air and ground sensor systems employed in an urban environment to collect data on moving/stationary vehicles and dismounts. A Layered Sensing processing framework is needed that combines information, gleans meaning from the collected data, and re-tasks sensors in a timely manner to garner actionable intelligence. Tasking for the numerous sensory assets must be coordinated to support automatic cross-cueing so that the right information is collected at the right time and place. Initially, this effort will focus on jointly using wide-area motion imagery (RF and EO) and study how this can feed forward and back from a seeker deployed for a reconnaissance targeting mission. This will be a challenging proof-of-concept demonstration of a multi-sensor data fusion framework for layered sensing. Ultimately we envision this contributing to the Advanced Recognition Capability (ARC) Lab at AFRL and multiple ISR platforms. BENEFIT: The successful completion of Phase I will result in the generation of a general scheme for fusing data across multiple air and ground sensors. This will solve a technical challenge necessary for the development of Layered Sensing systems, a key enabler for many DoD missions such as battlespace awareness.