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A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

Wei Quan¹, Jiancheng Fang

¹Novel Inertial Instrument and Navigation System Technology Laboratory, School of Instrumentation Science and Optoelectronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China. quanwei@buaa.edu.cn

Sensors (Basel, Switzerland)

|February 2, 2012

Summary

This summary is machine-generated.

A novel star recognition method uses the Adaptive Ant Colony (AAC) algorithm to improve star sensor performance. This approach enhances both the speed and success rate of stellar map identification.

Keywords:

ant colony algorithm guidance-star database star recognition star sensor

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Area of Science:

Astronomy and Astrophysics
Computer Science
Artificial Intelligence

Background:

Star sensors are crucial for spacecraft attitude determination.
Current star recognition methods face challenges in speed and accuracy.
The Adaptive Ant Colony (AAC) algorithm offers potential for optimization problems.

Purpose of the Study:

To develop a new star recognition method for star sensors.
To enhance the speed and success rate of star identification.
To leverage the parallel processing and path optimization capabilities of the AAC algorithm.

Main Methods:

A novel star recognition method based on the Adaptive Ant Colony (AAC) algorithm.
Stars are used as centers of circles with a radius defined by a specific angular distance.
The AAC algorithm's parallel processing calculates angular distances between star pairs within circles.
The optimal (shortest) path found by the AAC algorithm is used for stellar map recognition.

Main Results:

The proposed method achieves a 98% identification success rate at a position error of approximately 50 arcseconds.
This success rate surpasses the Delaunay identification method, which achieved 94%.
The identification time is significantly reduced, reaching up to 50 milliseconds.

Conclusions:

The Adaptive Ant Colony (AAC) based star recognition method offers superior performance compared to existing techniques.
The method demonstrates a notable increase in both identification accuracy and speed.
This advancement holds promise for improving the reliability of star sensors in space applications.