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Information Theoretic Bounds for Determining Optimum Aberration Strengths for Various Diversity Polynomials and Noise Statistics for Phase Diversity

机译:确定各种分集多项式的最佳像差强度的信息理论界和相位分集的噪声统计量

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摘要

In this paper, we will study the optimum bounds for various diversity polynomials. For the Poisson or Gaussian noise cases studied, we have found that the optimum bound for extended scenes does not depend on the nature of the noise statistics. There is a slight dependence of optimum diversity for point sources, however. We will show, further, that the bound for Gaussian noise sources is larger than that for Poisson noise for large scenes. This behavior is reversed for point sources.
机译:在本文中,我们将研究各种分集多项式的最优边界。对于研究的泊松或高斯噪声案例,我们发现扩展场景的最佳范围不取决于噪声统计的性质。但是,点源的最佳分集存在少许依赖性。我们还将进一步证明,对于大型场景,高斯噪声源的边界大于泊松噪声的边界。点源的行为与此相反。

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