The K-Vector ND and its Application to Building a Non-Dimensional Star Identification Catalog Conference Paper

abstract

• A multi-dimensional orthogonal range-searching algorithm, the multi-dimensional k-vector (K-Vector ND), is presented. The algorithm is analyzed and found to have an execution time that is independent of the size of the database, for well-distributed data sets. Numerical tests are performed to determine the performance advantage as compared to a Quad-Tree for the two-dimensional data set. Results range from break-even to a factor of 14, depending on the database size. The K-Vector ND is then applied to the problem of building a non-dimensional star-identification database that contains all visible star triples. The performance of the K-Vector ND algorithm in that task is then compared to a simple nested loop, and found to range from break-even to a factor of 200, depending on the size of the database.

authors

• Mortari, Daniele

published proceedings

• JOURNAL OF THE ASTRONAUTICAL SCIENCES

author list (cited authors)

• Spratling, B. B., & Mortari, D.

citation count

• 5

complete list of authors

• Spratling, Benjamin B||Mortari, Daniele

publication date

• April 2011

publisher

• Springer Nature  Publisher

published in

• Journal of the Astronautical Sciences  Journal

keywords

• 46 Information And Computing Sciences
• 4601 Applied Computing
• Prevention

Digital Object Identifier (DOI)

• 10.1007/BF03321168

start page

• 261

end page

• 274

volume

• 58

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1007%2Fbf03321168