Track Correlation

Several approaches to multi-target tracking using data from disparate sources are available as a point of departure for meeting the requirements of the ballistic missile defense system. Kalman filters (KF) represent the basic fusion approach geared towards tracking the 3D positions, velocities and accelerations of boosting missiles by fusing angles-only data from multiple sensors. Extended Kalman filters (EKF) and Interacting Multiple Models (IMM) are critical improvements to these fusion approaches, but armed with tracking data only they are not enough. It is clear that the problem cannot be solved simply by ingesting angles-angles data from a variety of disparate sensor sources into a boost phase tracker and “turning the crank.” The key to early intercept will be to discriminate between observations of multiple threats, eliminate false tracks, and characterize each threat prior to ingestion of angles-angles data into the EKF/IMM framework. SciTec’s will extract features that are available from signature data only during the early boost phase from OPIR to provide critical evidence of threat type and heading, fuse these features with track data to prioritize cueing/processing UAS data and develop a fusion processor that will ingest data from disparate sources to provide a track precise enough to support engagements.