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Related Experiment Video

Updated: May 26, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

A novel systematic error compensation algorithm based on least squares support vector regression for star sensor

Jun Yang¹, Bin Liang, Tao Zhang

¹Department of Automation, Tsinghua University, Beijing 100084, China. jun-yang07@mails.tsinghua.edu.cn

Sensors (Basel, Switzerland)

|December 14, 2011

Summary

This summary is machine-generated.

This study analyzes systematic errors in star centroid estimation for star sensors. A novel compensation algorithm significantly improves estimation accuracy, enhancing attitude determination precision.

Keywords:

LSSVR centroid estimation star sensor subpixel systematic error compensation

Related Experiment Videos

Last Updated: May 26, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Area of Science:

Spacecraft attitude determination
Optical sensor calibration
Astrodynamics

Background:

Star centroid estimation is critical for precise attitude determination in star sensors.
Existing algorithms are susceptible to systematic errors affecting accuracy.
Understanding and mitigating these errors is essential for reliable navigation.

Purpose of the Study:

To theoretically analyze systematic errors in star centroid estimation algorithms.
To develop a novel compensation algorithm for error reduction.
To improve the precision of star centroid estimation.

Main Methods:

Frequency domain analysis and numerical simulations to study systematic error.
Analysis of approximation and truncation errors due to discretization and sampling limitations.
Development of a least squares support vector regression (LSSVR) with Radial Basis Function (RBF) kernel for compensation.

Main Results:

Systematic error identified as approximation and truncation errors.
A criterion for selecting sampling window size to minimize truncation error is provided.
The LSSVR-based compensation algorithm improved accuracy from 0.06 to 6 × 10(-5) pixels for a 5-pixel window.

Conclusions:

Systematic errors in star centroid estimation are quantifiable and reducible.
The proposed LSSVR compensation algorithm effectively eliminates systematic errors.
This advancement significantly enhances the precision of star sensors for attitude determination.