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High range resolution radar signal classification a partitioned rough set approach

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2 Author(s)
D. E. Nelson ; Target Recognition Branch, Air Force Res. Lab., Wright-Patterson AFB, OH, USA ; J. A. Starzyk

In automatic target recognition (ATR) systems there are advantages to developing classifiers based on a portion of the signal. A partitioning technique is introduced in this paper that allows rough set theory to be applied to real-world size problems. Rough set theory (RST) is an emerging concept for determining features and then classifiers from a training data set. RST guarantees that once the data has been labeled all possible classifiers (based on that labeling) can be generated. There are multiple classifiers for each signal partition and multiple partitions for each signal. Classifiers based on a single reduct (classifier) or one partition do not perform well enough to be useful. We fuse all the reducts from all the partitions into one classifier. This fusion of partitioned reducts yields a synergistic result that produces a classifier with a high probability of declaration and good probability of correct classification

Published in:

System Theory, 2001. Proceedings of the 33rd Southeastern Symposium on

Date of Conference:

Mar 2001