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Tracker Performance Improvement Using Multiple-Sensor Data

The increase in target signature information inherent in multi-spectral techniques can be used to increase tracker knowledge of current and predicted target states. In addition to the target location, the velocity, acceleration states, and target aspect angle, for example, can be measured. Aspect angle can be used to limit the possible future states of the target (aircraft, for example, have maximum maneuverability along an axis perpendicular to the wing plane.) Aspect angle, coupled with information concerning maximum target acceleration capabilities, can be used to provide an 'envelope' of possible future target locations. This envelope and position prediction are updated with each observation of the target status. A target pattern recognition algorithm has been used to classify target images, estimating both object identification and aspect angle. A modified form of the moments invariant technique, combined with a maximum likelihood classifier, has been developed and tested against imagery acquired with a laboratory image acquisition and analysis system. Simulations of combined motion and aspect angle sensing trackers indicated enhanced performance over systems with kinematic information only. A database of target imagery has been created, and algorithm accuracy as a function of signal-to-noise ratio has been determined. The effects of several pre-processing steps on the electro-optical sensor aspect angle measurement have also been studied. Critical invariance properties have been examined in a limited number of tests.