Abstract
The performance of an automatic target recognition (ATR) algorithm is not only influenced by the relevance of the prior (training) information, but also by the level of difficulty posed by the clutter that surrounds the target. Thus, an objective measure of target- (or signal-) to-clutter ratio (SCR) is important for the assessment of ATRs. We describe a new metric for SCR based on an eigen analysis of a two-class problem. It is believed that an SCR metric, along with a measure for the relevancy of the training data, are the key parameters for the characterization of ATR performance. Various examples are given to illustrate the application of eigen analysis in determining the difficulty level of finding a particular target in the presence of clutter, and consequently how this new SCR metric defines the potential for false alarms.
Published Version
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