AS&T has been selected to receive an US Air Force Phase II SBIR grant to support development of its innovative technique of Target Characterization using Markov-chain Augmented Multiple-Modality Data Fusion
In order to establish a comprehensive picture of a situation of interest McMODAF employs a variety of sensors that combine data. This results in increased surveillance capabilities and effectiveness by giving a more complete, integrated picture of situations and enabling a quicker response while eliminating errors from potential failures of individual sensors. McMODAF technique is essential in providing effective identification, discrimination and tracking of the objects of interest whenever it is required to determine various aspects of its state and features for augmented space surveillance. In this program, AS&T will develop a decision-support toolbox with its operation based on the fusion of heterogeneous data generated by a set of imaging and non-imaging sensors with differing modalities. By designing and assembling an FPGA-based multi-sensor McMODAF platform, AS&T will be able to verify and optimize performance of the predictive system. The program will culminate with field demonstrations of the technology in an operational environment.