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13 May 2010A probability of error-constrained sequential decision algorithm for data-rich automatic target recognition
This paper illustrates an approach to sequential hypothesis testing designed not to minimize the amount of data collected
but to reduce the overall amount of processing required, while still guaranteeing pre-specified conditional probabilities
of error. The approach is potentially useful when sensor data are plentiful but time and processing capability are
constrained. The approach gradually reduces the number of target hypotheses under consideration as more sensor data
are processed, proportionally allocating time and processing resources to the most likely target classes. The approach is
demonstrated on a multi-class ladar-based target recognition problem and compared with uniform-computation tests.
Irwin O. Reyes,Michael D. DeVore,Peter A. Beling, andBarry M. Horowitz
"A probability of error-constrained sequential decision algorithm for data-rich automatic target recognition", Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 769615 (13 May 2010); https://doi.org/10.1117/12.858293
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Irwin O. Reyes, Michael D. DeVore, Peter A. Beling, Barry M. Horowitz, "A probability of error-constrained sequential decision algorithm for data-rich automatic target recognition," Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 769615 (13 May 2010); https://doi.org/10.1117/12.858293