A K-VECTOR APPROACH TO SAMPLING, INTERPOLATION, AND APPROXIMATION
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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.
author list (cited authors)
Mortari, D., & Rogers, J.