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Optimal Sensor Array Configuration in Remote Image Formation

机译:远程成像中的最佳传感器阵列配置

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摘要

Determination of optimal sensor configuration is an important issue in many remote imaging modalities, such as tomographic and interferometric imaging. In this paper, a statistical optimality criterion is defined and a search is performed over the space of candidate sensor locations to determine the configuration that optimizes the criterion over all candidates. To make the search process computationally feasible, a modified version of a previously proposed suboptimal backward greedy algorithm is used. A statistical framework is developed which allows for inclusion of several widely used image constraints. Computational complexity of the proposed algorithm is discussed and a fast implementation is described. Furthermore, upper bounds on the sum of the squared error of the proposed algorithm are derived. Connections of the method to the deterministic backward greedy algorithm for the subset selection problem are presented, and two application examples are described. Five compelling optimality criteria are considered, and their performance is investigated through numerical experiments for a tomographic imaging scenario. In all cases, it is verified that the configuration designed by the proposed algorithm performs better than wisely chosen alternatives.
机译:确定最佳传感器配置是许多远程成像模式(如断层成像和干涉成像)中的重要问题。在本文中,定义了统计最优性标准,并在候选传感器位置的空间上进行了搜索,以确定可优化所有候选标准的配置。为了使搜索过程在计算上可行,使用了先前提出的次优后向贪婪算法的修改版本。建立了统计框架,该统计框架允许包含多个广泛使用的图像约束。讨论了所提出算法的计算复杂度并描述了一种快速实现。此外,推导了所提出算法的平方误差之和的上限。给出了该方法与子集选择问题的确定性后向贪婪算法的联系,并描述了两个应用示例。考虑了五个令人信服的最佳标准,并通过针对层析成像场景的数值实验研究了它们的性能。在所有情况下,都可以验证所提出算法设计的配置比明智选择的配置具有更好的性能。

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