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Optimal uniform guide star catalog using a genetic algorithm.

Farshad Somayehee, Masoud Ebrahimi, Amir Ali Nikkhah

|September 14, 2023

Summary

This summary is machine-generated.

This study created an optimal guide star catalog for star sensors, reducing star numbers while increasing the probability of identifying at least 5 stars for reliable navigation.

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Area of Science:

Astronomy
Astrodynamics
Spacecraft Navigation

Background:

Star sensors are crucial for spacecraft navigation but face onboard hardware and software limitations.
Existing guide star catalogs may not be optimal for real-time onboard processing.
Reliable star identification requires a sufficient number of stars within the sensor's field of view (FOV).

Purpose of the Study:

To develop an optimal uniform guide star catalog for enhanced star sensor performance.
To address limitations in onboard processing by optimizing catalog generation.
To improve the reliability of star identification algorithms in spacecraft.

Main Methods:

Defined an objective function based on field of view (FOV) and magnitude threshold.
Employed a genetic algorithm combined with Latinized stratified sampling.
Developed a novel spherical density determination algorithm for star distribution.
Utilized Monte Carlo simulations for validation.

Main Results:

Achieved a reduction in the number of stars required in the uniform catalog.
Increased the probability of observing at least 5 stars for star identification to 98.34%.
Validated results demonstrating dramatic improvements in catalog optimality and reliability.

Conclusions:

The optimized uniform guide star catalog significantly enhances star sensor reliability.
The novel algorithms and methods provide a robust solution for onboard star identification.
This approach overcomes limitations, enabling more precise and dependable spacecraft navigation.