Automatic target recognition using kinematic priors

Traditional automatic target recognition (ATR) systems discriminate based upon target size, target shape, or both. In this paper, an ATR algorithm is proposed that exploits aircraft-class specific kinematics to assess the tracked target's likelihood. Prior information on kinematics includes the physical parameters of the aircraft, allowable input forces to a pilot, and pilot behavior in the aircraft. It is shown that the computation of the likelihood of observed events is intractable. A suboptimal approximation to the likelihood can be computed using a hypothesis reduction method based on the generalized pseudo-Bayesian (GPB) class of algorithms. A bound on the L/sub 1/ distance of a suboptimal approximate density from the true density is derived.<
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