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

David Rijlaarsdam,Hamza Yous,Davide Oddenino,Gianluca Furano,Jonathan Byrne,David Moloney

doi:10.3390/s20092579

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.

Highlights

Accurate attitude determination for spacecraft remains a relevant and often researched topic due to the progress in spacecraft technology of the past few decades
Since 2009, significant advancements have been made in star identification algorithms; some truly novel approaches have been proposed
We have identified two categories that cover all feature extraction methods that can be used to classify star identification algorithms

Summary

Introduction

Accurate attitude determination for spacecraft remains a relevant and often researched topic due to the progress in spacecraft technology of the past few decades. Even though there are numerous sensor options to determine the attitude of a spacecraft, the most accurate sensor is the star sensor [2,3], which is becoming the most widely applied sensor in spacecraft attitude determination [4]. One of the most important components of the star sensor system is the star identification algorithm. This algorithm is part of the data processing function of the star sensor and identifies which stars are present in a specific scene

Objectives

Methods

Findings

Conclusion