The paper considers the following problem: given a 3D model of a reference target and a sequence of images of a 3D
scene, identify the object in the scene most likely to be the reference target and determine its current pose. Finding the
best match in each frame independently of previous decisions is not optimal, since past information is ignored. Our
solution concept uses a novel Bayesian framework for multi target tracking and object recognition to define and
sequentially update the probability that the reference target is any one of the tracked objects. The approach is applied to
problems of automatic lock-on and missile guidance using a laser radar seeker. Field trials have resulted in high target hit
probabilities despite low resolution imagery and temporarily highly occluded targets.