Proceedings Article | 1 August 2002
KEYWORDS: Kinematics, Computed tomography, Detection and tracking algorithms, Radar, Signal to noise ratio, Error analysis, Synthetic aperture radar, Roads, Automatic target recognition, Sensors
Airborne radars tasked with tracking moving targets face the challenge of surveillance over large geographic areas where military vehicles will be interspersed with civilian traffic. There is a major need to develop robust, efficient, and reliable identification and tracking techniques to identify selected targets, and to maintain tracks for selected, critical targets, in dense target environments. Traditionally, tracking and identification have been considered separately - one may identify a target, and then track it kinematically to sustain the identification. The difficulty with separate identification and tracking is that neither is sufficient by itself to satisfy the demands of the other. Identification techniques applied to moving targets require some degree of evidence accrual, which requires the kinematic tracker to have a high degree of fidelity. Conversely, kinematic association could be aided considerably with high confidence single-look identification, but high confidence identification only builds up with several looks. What is needed is to incorporate the distinctive target signature information into the tracker, so that identification and tracking, or signature comparison and tracking, perform together as a unit. Kinematic moving target indication (MTI) trackers receive reports in the form of ground coordinates and Doppler (range rate) and attempt to maintain track of the moving targets by associating the reports to target tracks. In situations where different targets exhibit similar kinematics, the association logic used for track-report association may not yield the correct pairings. In such complex and challenging environments, the additional information arising from distinctive target signatures can be used to aid the tracking algorithms. The approach to moving target identification and feature-aided tracking (FAT) described here combines accumulated target classification information, obtained from HRR, with kinematic association scores to yield improved classification and improved association. When the target under track is one of a set for which classification templates are available, then the association between the reports and tracks can be aided by the similarity in identification between the report and track. When such information is not available, and therefore classification is not performed, then the association is aided by the similarity in radar signature between the report and track. In this paper, we will describe the basics of moving target classification and signature comparison. We will describe how this information may be incorporated into kinematic tracking.
