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"DTIC ADA601482: Bayesian Methods And Confidence Intervals For Automatic Target Recognition Of SAR Canonical Shapes" and the language of the book is English.


“DTIC ADA601482: Bayesian Methods And Confidence Intervals For Automatic Target Recognition Of SAR Canonical Shapes” Metadata:

  • Title: ➤  DTIC ADA601482: Bayesian Methods And Confidence Intervals For Automatic Target Recognition Of SAR Canonical Shapes
  • Author: ➤  
  • Language: English

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  • Internet Archive ID: DTIC_ADA601482

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"DTIC ADA601482: Bayesian Methods And Confidence Intervals For Automatic Target Recognition Of SAR Canonical Shapes" Description:

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This research develops a new Bayesian technique for the detection of scattering primitives in synthetic aperture radar (SAR) phase history data received from a sensor platform. The primary goal of this research is the estimation of size, position, and orientation parameters defined by the ?canonical? shape primitives of Jackson. Previous Bayesian methods for this problem have focused on the traditional maximum a posteriori (MAP) estimate based on the posterior density. A new concept, the probability mass interval, is developed. In this technique the posterior density is partitioned into intervals, which are then integrated to form a probability mass over that interval using the Gaussian quadrature numerical integration techniques. The posterior density is therefore discretized in such a way that the location of local peaks are preserved. A formal treatment is given to the effect of locally integrating the posterior density in the context of parameter estimation. It is shown that the operation of choosing the interval with the highest probability mass is equivalent to an optimum Bayesian estimator that places zero cost on a ?range? of parameters. The range is user-controlled, and is akin to the idea of parameter resolution. Additionally the peak-preserving property allows the user to begin with coarse intervals and ?zoom? in as they see fit. Associated with these estimates is a measure of quality called the credible interval (or credible set). The credible interval (set) is a region of parameter space where the ?true? parameter is located with a user-defined probability. Narrow credible intervals are associated with high-quality estimates while wide credible intervals are associated with poor estimates. The techniques are implemented in state-of-the-art graphics processor unit (GPU) hardware, which allows the numerical integration to be performed in a reasonable time. A typical estimator requires several hundred million computations and the GPU implementa

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