Knowledge- and model-based automatic target recognition algorithm adaptation

Firooz A. Sadjadi; Hatem N. Nasr; Hossien Amehdi; Michael E. Bazakos

doi:10.1117/12.55788

1 February 1991 Knowledge- and model-based automatic target recognition algorithm adaptation

Firooz A. Sadjadi, Hatem N. Nasr, Hossien Amehdi, Michael E. Bazakos

Author Affiliations +

Optical Engineering, Vol. 30, Issue 2, (February 1991). https://doi.org/10.1117/12.55788

Abstract

One ofthe most critical problems in automatic target recognition (ATR) systems is multiscenario adaptation. Today's ATR systems perform unpredictably, i.e., perform well in certain scenarios and poorly in others. Unless ATR systems can be made adaptable, their utility in battlefield missions remains questionable. We have developed a novel method called knowledge- and model-based algorithm adaptation (KMBAA). KMBAA automatically adapts the ATR parameters as the scenario changes so that ATR can maintain optimum performance. The KMBAA approach has been tested with a nonreal-time ATR simulation system and has demonstrated a significant improvement in detection, false alarm rate reduction, and segmentation accuracy performance.

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Firooz A. Sadjadi, Hatem N. Nasr, Hossien Amehdi, and Michael E. Bazakos "Knowledge- and model-based automatic target recognition algorithm adaptation," Optical Engineering 30(2), (1 February 1991). https://doi.org/10.1117/12.55788

Published: 1 February 1991

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