Sieve Search Centroiding Algorithm for Star Sensors.
Vivek Chandran Karaparambil1, Narayan Suresh Manjarekar1, Pravin Madanrao Singru1
1Birla Institute of Technology and Science, Pilani, K. K. Birla Goa Campus, Sancoale 403726, Goa, India.
Sensors (Basel, Switzerland)
|March 30, 2023
Summary
A new sieve search algorithm (SSA) accurately estimates star image centroids for attitude estimation. This robust method offers precision comparable to fitting algorithms with reduced computational cost, ideal for small satellites.
Area of Science:
- Spacecraft attitude determination
- Image processing for astrodynamics
Background:
- Accurate attitude estimation is critical for spacecraft.
- Centroid localization of star images on sensor arrays is a key factor.
Purpose of the Study:
- To introduce an intuitive self-evolving centroiding algorithm, the sieve search algorithm (SSA).
- To evaluate SSA's performance and suitability for resource-constrained small satellites.
Main Methods:
- Mapping star image gray-scale distribution into a matrix.
- Segmenting the matrix into 'sieves' for evaluation based on symmetry and magnitude.
- Calculating the centroid as a weighted average of pixel scores.
Main Results:
- SSA demonstrates effectiveness across varied star image conditions (brightness, noise, PSF).
- Achieves precision comparable to fitting algorithms with significantly lower computational overhead.
- Exhibits robustness in challenging scenarios like non-uniform PSFs and double stars.
Conclusions:
- SSA provides a robust and computationally efficient solution for star image centroiding.
- It represents a balanced approach between precision, robustness, and processing time for small satellite applications.
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