Abstract: This paper describes two practical fusion techniques for automatic target cueing that combine features derived from each sensor data ta the object-level. In the hybrid fusion method each of the input sensor data is prescreened before the fusion stage. The cued fusion method assumes that one of the sensors is designated as a primary sensor, and thus ATC is only applied to its input data. If one of the sensors exhibits a higher Pd and/or a lower false alarm rate, it can be selected as the primary sensor. However, if the ground coverage can be segmented to regions in which one of the sensors is known to exhibit better performance, then the cued fusion can be applied locally/adaptively by switching the choice of a primary sensor. Otherwise, the cued fusion is applied both ways and the outputs of each cued mode are combined. Both fusion approaches use a back-end discrimination stage that is applied to a combined feature vector to reduce false alarms. The two fusion processes were applied to spectral and radar sensor data nd were shown to provide substantial false alarm reduction. The approaches are easily extendable to more than two sensors. !4
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