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

Area of Science:

• Astronomy
• Aerospace Engineering
• Computer Science

Background:

• Lost-in-space star identification is vital for autonomous spacecraft navigation.
• Existing surveys provide a foundation, but recent advancements require updated reviews.
• Current algorithms are critical components of star sensor systems.

Purpose of the Study:

• To extend the 2009 survey of lost-in-space star identification algorithms.
• To provide a qualitative overview of current research in the field.
• To define a taxonomy of algorithms based on feature extraction methods.

Main Methods:

• Literature review and survey of recent lost-in-space star identification algorithms.
• Development of a taxonomy categorizing algorithms by feature extraction techniques.
• Analysis of comparative studies to identify inconsistencies in performance evaluation.

Main Results:

• A comprehensive survey of recent lost-in-space star identification algorithms is presented.
• A novel taxonomy classifying algorithms based on feature extraction is defined.
• Inconsistencies in the comparative evaluation of algorithms in current literature are identified.

Conclusions:

• The current literature presents challenges in reliably comparing lost-in-space star identification algorithms.
• Standardized simulation considerations are proposed to mitigate evaluation inconsistencies.
• This work provides a foundation for more consistent and reliable algorithm performance assessment.