Star Tracker Rate Estimation with Kalman Filter
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Solution Overview
Problem
Spacecraft attitude control systems face challenges in system stability and fault autonomy due to the absence or failure of gyros, leading to unreliable rate estimates from current star trackers, which can result in degraded mission performance.
Innovation Solution
The implementation of enhanced Kalman filtering methods that average forward and backward propagations of state and error covariances, allowing for accurate estimation of spacecraft body rates using star tracker measurements without gyro information, thereby improving attitude control and sensor calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If star tracker is used to estimate spacecraft rates without gyros, then spacecraft cost is reduced, but rate estimation reliability deteriorates
Solution Approach 1:
The patent replaces the mechanical gyroscope system with a star tracker-based computational method. Instead of using physical gyros to measure angular rate directly, the system uses star tracker measurements of star position changes over time, combined with Kalman filtering algorithms, to compute spacecraft attitude and rate information. This substitution eliminates the need for expensive gyroscopic hardware while providing reliable rate estimates through sophisticated signal processing and estimation theory.
2Device complexity
If standard star tracker rate estimation is used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent implements a Kalman filter-based feedback mechanism that continuously refines rate estimates based on star tracker measurements. The Kalman filter processes measurements of star position changes over time, using feedback from previous estimates and measurements to converge on accurate spacecraft rate values. This feedback-driven approach enables high-precision rate measurement without requiring complex hardware systems, as the computational algorithm adapts to measurement noise and uncertainties iteratively.
Data Source
AI summary
An attitude estimator that uses star tracker measurements and enhanced Kalman filtering, with or without attitude data, to provide three-axis rate estimates. The enhanced Kalman filtering comprises taking an average of forward and rearward propagations of the Kalman filter states and the error covariances. The star tracker-based rate estimates can be used to control the attitude of a satellite or to calibrate a sensor, such as a gyroscope.