Attitude estimation of earth orbiting satellites by decomposed linear recursive filters

Attitude estimation of earth orbiting satellites (including Large Space Telescope) subjected to environmental disturbances and noises was investigated. Modern control and estimation theory is used as a tool to design an efficient estimator for attitude estimation. Decomposed linear recursive filters for both continuous-time systems and discrete-time systems are derived. By using this accurate estimation of the attitude of spacecrafts, state variable feedback controller may be designed to achieve (or satisfy) high requirements of system performance

Performance Comparison of Particle Filter in Small Satellite Attitude Estimation

The drive towards miniaturization, coupled with the latest advances in onboard processing, has given rise to small satellite missions’ ability to use more complex attitude estimation algorithms to fit their progressive mission requirements. Earth observation missions typically require higher satellite attitude pointing accuracies to precisely control the satellite orientation. Hence, to provide greater confidence in the attitude estimation accuracies, new advanced algorithms are continuously being developed. Satellite attitude estimation must be performed autonomously in real-time whilst optimizing computational resources such as time and memory. Small satellite missions with higher complexities tend to demand more sophisticated requirements, which push the limits of classical attitude estimation methods. The Particle Filter is an advanced Bayesian estimation technique that has shown significant improvements in satellite attitude estimation. This work describes the Particle Filter and its implementation to the attitude and angular rate estimation for a 3U CubeSat in Low Earth Orbit, whilst comparing attitude estimation performance in two different settings: with three-axis magnetometer measurements; and with combined measurements from a three-axis magnetometer and sun sensors. This work further reports that for attitude determination in small satellites, the Particle Filter is a more accurate attitude estimator than the widely used Extended Kalman Filter. The Particle Filter yields attitude estimation accuracy of ±0.01°, while the Extended Kalman Filter attitude estimation accuracy is ±1°. Moreover, the results indicate that the use of an additional sensor improves the attitude estimation accuracy of the Particle Filter by 17%. It is essential to consider different sensor combinations as it helps select the most suitable sensor suite and attitude estimator for an individual small satellite mission

Autonomous attitude estimation via star sensing and pattern recognition

Results are reported on the development of an autonomous, onboard, near real time spacecraft attitude estimation technique. The approach uses CCD based star sensors to digitize relative star positions. Three microcomputers are envisioned, configured in parallel, to: (1) determine star image centroids and delete spurious images; (2) identify measured stars with stars in an onboard catalog and determine discrete attitude estimates; (3) integrate gyro rate measurements and determine optimal real time attitude estimates for use in the control system and for feedback to the star identification algorithm. Algorithms for the star identification are presented. The discrete attitude estimation algorithm recovers thermally varying interlock angles between two star sensors. The optimal state estimation process recovers rate gyro biases in addition to real time attitude estimates