An Information-Theory Based Feature Aided Tracking and Identification Algorithm for Tracking Moving and Stationary targets through High Turn Maneuvers using Fusion of SAR and HRR Information

Tracking and identification algorithms have been developed to track moving targets using high-range resolution (HRR) radar. Likewise algorithms exist to link moving target indicator (MTI) hits with synthetic aperture radar (SAR) images to follow targets that are in a move-stop-move scenario. Each of these algorithms have limitations in their abilities to maintain a consistent track of an object that is in a move-stop-maneuver scenario. For example, (1) there is only spatial information from which to link MTI hits with SAR data and in the other case (2) HRR track and ID algorithm would not capture stationary targets. Fusing these modes can provide for track consistency. When multiple targets exist, such as in a group tracking scenario, the spatial information to link moving and stationary returns would be difficult. The incorporation of moving HRR classification and stationary target identification information would enhance the MTI-SAR linking algorithm for multiple targets. While HRR is better suited than MTI for linking object track information to spatially stationary SAR information, the difficulty with relying solely on HRR information is that when a target goes through a maneuver in which it is turning, it may be temporarily stopped in a static-rotator case. This paper discusses an information-theory approach for identification of targets in 1-D HRR and 2-D SAR modes and its incorporation into a feature-aided tracking and ID algorithm for tracking a target which goes through a stationary, moving, and maneuvering dynamics. Results are presented for a group of highly maneuvering targets which travel in one direction, turn, and travel in another direction from which typical kinematic tracking algorithms based on HRR information would break down.