Automated Target Characterization and Correlation with Heterogeneous Kinematic and Feature Data for Sensor Handover
Automatic Target Characterization (ATC)
ANDRO looks to develop and implement a set of algorithms within a unified target characterization and correlation framework capable of operating in a multiple heterogeneous sensor environment where detection, classification, localization and track priority information is exchanged among multiple platforms. Our goal is to deliver an effective automated and autonomous information extraction and fusion system that can be incorporated in today’s operational systems. The primary focus will be on the development of algorithms for target characterization and correlation that can handle the difficult track handover between EO/IR boost phase detection sensors and weapon control sensors.
Information fusion with heterogeneous sensors is challenging because non-kinematic features are different for each sensor type making it is difficult to correlate features across sensors. Thus, it is necessary to develop meta-features that are sensor invariant and amenable to optimal track correlation across sensors. Once this target characterization is carried out effectively, the next task is to develop efficient fusion or correlation algorithms that can yield better common tracks and facilitate accurate track hand-over. In our approach, target characterization (information extraction) and correlation (information fusion) are tightly coupled problems that are addressed jointly to ensure optimal overall performance.