Abstract
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.
Highlights
Star sensor is widely used as an attitude determination sensor in satellite since the star sensor is a most accurate satellite attitude determination sensor (Bak, 1999; Mohammadnejad et al, 2012)
If the star identification process does not find any stars matched in the catalog, the star sensor failed in identifying stars
In order to reduce the number of stars for star catalog generation purpose, it requires a method to identify regions of a high star density reduce a number of them
Summary
Star sensor is widely used as an attitude determination sensor in satellite since the star sensor is a most accurate satellite attitude determination sensor (Bak, 1999; Mohammadnejad et al, 2012). This method has easy to apply, but this method is constrained in the uneven distribution of stars that allow the emergence of a "hole" in any FOV bore sight direction In these conditions, the star sensor failed in identifying stars and affecting the attitude determination of the satellite. In order to reduce the number of stars for star catalog generation purpose, it requires a method to identify regions of a high star density reduce a number of them This process is repeated to obtain the number of stars in which the simulation in any FOV direction always gets at least three stars as a condition star pattern recognition by using triangle algorithm. TM did not consider the visual magnitude threshold that can be detected by star sensor
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