General Metropolis-Hastings jump diffusions for automatic target recognition in infrared scenes

Aaron D. Lanterman; Michael I. Miller; Donald L. Snyder

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General Metropolis-Hastings jump diffusions for automatic target recognition in infrared scenes

机译：General Metropolis-Hastings jump diffusions for automatic target recognition in infrared scenes

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To locate and recognize ground-based targets in forward-looking IR (FLIR) images, 3-D faceted models with associated pose parameters are formulated to accommodate the variability found in FLIR imagery. Taking a Bayesian approach, scenes are simulated from the emissive characteristics of the CAD models and compared with the collected data by a likelihood function based on sensor statistics. This likelihood is combined with a prior distribution defined over/he set of possible scenes to form a posterior distribution. To accommodate scenes with variable numbers of targets, the posterior distribution is defined over parameter vectors of varying dimension. An inference algorithm based on Metropolis-Hastings jump-diffusion processes empirically samples from the posterior distribution, generating configurations of templates and transformations that match the collected sensor data with high probability. The jumps accommodate the addition and deletion of targets and the estimation of target identities; diffusions refine the hypotheses by drifting along the gradient of the posterior distribution with respect to the orientation and position parameters. Previous results on jumps strategies analogous to the Metropolis acceptance/rejection algorithm, with proposals drawn from the prior and accepted based on the likelihood, are extended to encompass general Metropolis-Hastings proposal densities. In particular, the algorithm proposes moves by drawing from the posterior distribution over computationally tractible subsets of the parameter space. The algorithm is, illustrated by an implementation on a Silicon Graphics Onyx/Reality Engine. # 1997 Society of Photo-Optical Instrumentation Engineers. S0091-3286(97)02404-5 Subject terms: automatic target recognition; pattern theory; jump diffusion; infrared; forward-looking infrared. Paper 05086 received Aug. 3, 1996; accepted for publication Nov, 12, 1996.

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《Optical Engineering》 |1997年第6期|1123-1137|共15页
作者
Aaron D. Lanterman; Michael I. Miller; Donald L. Snyder;
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Washington University Department of Electrical Engineering Electronic Systems and Signals Research Laboratory/ Campus Box 1161 St. Louis, Missouri 63130-4899 E-mail: adl@essrl.wustl.edu;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类光学仪器;
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General Metropolis-Hastings jump diffusions for automatic target recognition in infrared scenes

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