ROBUST TRIAD AND QUAD GENERATION ALGORITHMS FOR STAR TRACKERS

Star Identification (Star-ID) is a complex problem, mainly because some of the observations are not generated by actual stars, but by reflecting debris, other satellites, visible planets, or by electronic noise. For this reason, the capability to discriminate stars from non-stars is an important aspect of Star-ID robustness. Usually, the Star-ID task is performed by first attempting identification on a small group of observed stars (a kernel) and, in case of failure, replacing that kernel with another until a kernel made only of actual stars is found. This work performs a detailed analysis of kernel generator algorithms, suitable for onboard implementation in terms of speed and robustness, for kernels of three (triad) and four (quad) stars. Three new kernel generator algorithms and, in addition to the existing expected time to discovery, three new metrics for robustness evaluation are proposed. The proposed algorithms are fast, robust to find good kernels, and do not require pre-stored data.

ROBUST TRIAD AND QUAD GENERATION ALGORITHMS FOR STAR TRACKERS Conference Paper