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首页> 外文期刊>The Journal of the Astronautical Sciences >A k-Vector Approach to Sampling, Interpolation, and Approximation
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A k-Vector Approach to Sampling, Interpolation, and Approximation

机译：采样，内插和逼近的k矢量方法

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摘要

The k-vector search technique is a method designed to perform extremely fast range searching of large databases at computational cost independent of the size of the database. k-vector search algorithms have historically found application in satellite star-tracker navigation systems which index very large star catalogues repeatedly in the process of attitude estimation. Recently, the k-vector search algorithm has been applied to numerous other problem areas including non-uniform random variate sampling, interpolation of 1-D or 2-D tables, nonlinear function inversion, and solution of systems of nonlinear equations. This paper presents algorithms in which the k-vector search technique is used to solve each of these problems in a computationally-efficient manner. In instances where these tasks must be performed repeatedly on a static (or nearly-static) data set, the proposed k-vector-based algorithms offer an extremely fast solution technique that outperforms standard methods.
机译：k向量搜索技术是一种设计用于以与数据库大小无关的计算成本执行大型数据库超快范围搜索的方法。从历史上看，k矢量搜索算法已应用于卫星恒星跟踪器导航系统中，该系统在姿态估计过程中反复索引非常大的恒星目录。最近，k矢量搜索算法已应用于许多其他问题领域，包括非均匀随机变量采样，一维或二维表的插值，非线性函数反演以及非线性方程组的解。本文提出了一种算法，其中k矢量搜索技术用于以计算有效的方式解决这些问题。在必须在静态（或接近静态）数据集上重复执行这些任务的情况下，建议的基于k向量的算法提供了一种超快速的解决方案技术，其性能优于标准方法。

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