Literature DB >> 32369986

A Survey of Lost-in-Space Star Identification Algorithms since 2009.

David Rijlaarsdam1, Hamza Yous1, Jonathan Byrne1, Davide Oddenino2, Gianluca Furano2, David Moloney1.   

Abstract

The lost-in-space star identification algorithm is able to identify stars without a priori attitude information and is arguably the most critical component of a star sensor system. In this paper, the 2009 survey by Spratling and Mortari is extended and recent lost-in-space star identification algorithms are surveyed. The covered literature is a qualitative representation of the current research in the field. A taxonomy of these algorithms based on their feature extraction method is defined. Furthermore, we show that in current literature the comparison of these algorithms can produce inconsistent conclusions. In order to mitigate these inconsistencies, this paper lists the considerations related to the relative performance evaluation of these algorithms using simulation.

Entities:  

Keywords:  attitude estimation; star feature extraction; star identification; star tracker algorithms

Year:  2020        PMID: 32369986     DOI: 10.3390/s20092579

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  1 in total

1.  Lunar Crater Identification in Digital Images.

Authors:  John A Christian; Harm Derksen; Ryan Watkins
Journal:  J Astronaut Sci       Date:  2021-10-21       Impact factor: 1.531

  1 in total

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