ENHANCEMENTS TO THE K-VECTOR SEARCH TECHNIQUE
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abstract
The k-vector range search technique has been applied to single and multidimensional databases. The k-vector has seen extensive success in the star tracking literature as an optimal time range search technique for determining candidate matches for star identification. Unfortunately, nonlinearities in the databases slow down this range searching technique. To avoid this problem, two enhancements to this technique which increase the average performance of the k-vector technique for nonlinear and dynamic databases, are presented. The analytical gain in performance with respect to database nonlinearities is discussed along with an information theoretic approach to database partitioning which allows the enhanced k-Vector to apply locally resulting in effective piecewise linear k-vectors.