Star Catalog Generation for Satellite Attitude Navigation Using Density Based Clustering
Muhammad Arif Saifudin, Bib Paruhum Silalahi and Imas Sukaesih Sitanggang
DOI : 10.3844/jcssp.2015.1082.1089
Journal of Computer Science
Volume 11, Issue 12
A new method to generate star catalog using density-based clustering is proposed. It identifies regions of a high star density by using Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. Reducing the number stars performed by storing the brightest star in each cluster. The brightest star and all non-clustered members are then stored as a navigation star candidate. Monte Carlo simulation has performed to generate random FOV to check the uniformity of the new catalog. Succeed parameter is if there are at least three stars in the FOV. The simulation results compare between DBSCAN method and Magnitude Filtering Method (MFM) which is the common method to generate star catalog. The result shows that DBSCAN method is better than MFM such for number of star 846 DBSCAN has success 100% while MFM 95%. It concluded that density-based clustering is a promising method to select navigation star for star catalog generation.
© 2015 Muhammad Arif Saifudin, Bib Paruhum Silalahi and Imas Sukaesih Sitanggang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.