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Performance Prediction of Quantization Based Automatic Target Recognition Algorithms

Horvath, Matthew Steven
http://rave.ohiolink.edu/etdc/view?acc_num=wright1452086412

Abstract Details

2015, Doctor of Philosophy (PhD), Wright State University, Engineering PhD.
The difficulty of designing automatic target recognition (ATR) systems is that there are many sources of potential variation in the data, often referred to as operating conditions (OCs). Typical evaluation methodologies rely on empirical simulations on fixed datasets, where it can be difficult to fully sample the variations the algorithm will see in application. Here we focus on analytic performance prediction approaches to quantization based algorithms where the sources of variation are assumed conditionally independent. We have focused on three algorithms in particular: multinomial pattern matching (MPM), quantized grayscale matching (QGM), and a quantized mean-squared error approach (QMSE). The first two are known as model-based ATR algorithms and assume that in-class images are the result of realizations of a statistical model with class-conditional parametrizations. The last is a template- based algorithm which assumes a deterministic “mean” image is available with which to compare candidate targets. We then study performance prediction approaches to these algorithms under a baseline AWGN noise case applicable to both SAR and IR imagery, an occlusion case applicable to IR imagery, and an individual point response (IPR) case applicable to SAR imagery.
Brian Rigling, Ph.D. (Advisor)
Fred Garber, Ph.D. (Committee Member)
Mateen Rizki, Ph.D. (Committee Member)
Michael Raymer, Ph.D. (Committee Member)
Mark Oxley, Ph.D. (Committee Member)
166 p.
Engineering
SAR; ATR; Performance prediction

Recommended Citations

Citations

  • Horvath, M. S. (2015). Performance Prediction of Quantization Based Automatic Target Recognition Algorithms [Doctoral dissertation, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1452086412

    APA Style (7th edition)

  • Horvath, Matthew. Performance Prediction of Quantization Based Automatic Target Recognition Algorithms. 2015. Wright State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1452086412.

    MLA Style (8th edition)

  • Horvath, Matthew. "Performance Prediction of Quantization Based Automatic Target Recognition Algorithms." Doctoral dissertation, Wright State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1452086412

    Chicago Manual of Style (17th edition)

wright1452086412
1,021
© 2015, all rights reserved.
This open access ETD is published by Wright State University and OhioLINK.