@Article{ArnasFialMort:2017:RoTrQu,
author = "Arnas, David and Fialho, M{\'a}rcio Afonso Arimura and Mortari,
Daniele",
affiliation = "{Centro Universitario de la Defensa Zaragoza} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Texas A \& M
University}",
title = "Robust triad and quad generation algorithms for star trackers",
journal = "Advances in the Astronautical Sciences",
year = "2017",
volume = "160",
pages = "859--878",
note = "27th AAS/AIAA Space Flight Mechanics Meeting, 2017; San Antonio;
United States; 05-09 Feb. 2017.",
abstract = "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.",
issn = "0065-3438.",
language = "en",
targetfile = "arnas_robust.pdf",
urlaccessdate = "28 abr. 2024"
}