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3D node localization from node-to-node distance information using cross-entropy method

机译:使用交叉熵方法从节点到节点距离信息进行3D节点定位

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This paper proposes a 3D node localization method that uses cross-entropy method for the 3D modeling system. The proposed localization method statistically estimates the most probable positions overcoming measurement errors through iterative sample generation and evaluation. The generated samples are evaluated in parallel, and then a significant speedup can be obtained. We also demonstrate that the iterative sample generation and evaluation performed in parallel are highly compatible with interactive node movement.
机译:本文提出了一种将交叉熵方法用于3D建模系统的3D节点定位方法。所提出的定位方法通过迭代生成和评估样本,统计估计克服测量误差的最可能位置。并行评估生成的样本,然后可以获得明显的加速。我们还演示了并行执行的迭代样本生成和评估与交互式节点移动高度兼容。

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