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7 August 2002 Target classification with data from multiple sensors
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The methods used in the classification of multiple small targets can be very different from the methods commonly used in traditional pattern recognition. First, there may be characteristics of the features for each target class that can permit simpler computations than other features. In addition, in classifying targets, the target tracks are updated as new data becomes available and hence there can be a sequence of feature measurements that are available for the target classification process. In addition, with multiple targets, the a priori information may be in a form that make the classification processing for one target dependent on the classification processing of other targets. These aspects of target classification that make that processing different from traditional pattern recognition are the concern of this paper. To limit the length of the paper, the scope is restricted to classification tasks that allow the linear-Gaussian assumption to be used. Also, the data used in the classification process is restricted to features, i.e., no attributes, and the assumption is the tracker does not employ feature-aided tracking. While these assumptions simplify the discussion, the methods used could be modified to permit classification of a broad scope of classification tasks.
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Oliver E. Drummond "Target classification with data from multiple sensors", Proc. SPIE 4728, Signal and Data Processing of Small Targets 2002, (7 August 2002);

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