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

The development and performance evaluation of Particle Filter–H∞ Filter for attitude estimation of a small satellite in low earth orbit

The accurate determination of the attitude of small satellites is essential for missions with stringent pointing requirements. Small satellites such as CubeSats are subject to mass and volume constraints, which directly affects their ability to carry more extensive and demanding payloads and limits their power and computational capabilities. There is, therefore, a need to minimise the computational cost of attitude determination while maximising accuracy and robustness to make small satellites useful for a wide range of applications. This research investigates the use of low-cost commercial sensors for attitude determination, namely the three-axis magnetometer and sun sensors, and proposes a novel hybrid Particle Filter–H∞ Filter to facilitate higher performance in the attitude determination of CubeSats. Existing attitude determination methods based on Kalman Filters are often not robust enough, which reduces their estimation accuracy, and they are limited in their inability to handle non-Gaussian noise. While Particle Filters offer higher estimation accuracy and the ability to handle non-Gaussian noise, their high computational cost is a distinct disadvantage. This research focuses on the development of a novel attitude estimator, the Particle Filter–H∞ Filter, which aims to achieve high estimation accuracy, robustness and lower computational cost by incorporating an H∞ Filter into the resampling step of a Particle Filter. The novel Particle Filter–H∞ Filter was developed and executed in a MATLAB simulation environment, in which an attitude determination system combining the relevant satellite environment models (the Simplified General Perturbations 4 orbit propagation model, the International Geomagnetic Reference Field model, the Sun position model), as well as three-axis magnetometer and sun sensor models and Euler’s equations for rigid body dynamics, including different simulation scenarios for a 3U CubeSat in low Earth orbit, was examined to verify the novel estimator. The simulation scenarios involve evaluating the estimator’s performance under different noise assumptions: Gaussian, non-Gaussian, increased process noise and coloured noise. Also, other simulation scenarios considered are magnetometer-only attitude estimation, large initial errors (observed during satellite separation and detumbling phase), the presence of measurement outliers and attitude estimation in a 6U CubeSat platform. The performance of the novel Particle Filter–H∞ Filter was evaluated against widely used nonlinear attitude estimators, namely the Extended Kalman Filter, the Unscented Kalman Filter, the H∞ Filter, and the standard Particle Filter, to determine its superiority with respect to the selected performance criteria: accuracy, robustness, computation time, and the ability to converge quickly and maintain convergence under different operating conditions. Compared to Particle Filters, the Kalman Filter-based attitude estimators are less computationally intensive, but their estimation accuracy is relatively poor. The results of various simulated scenarios have shown that the novel Particle Filter–H∞ Filter significantly improves the attitude estimation accuracy and has higher robustness compared to existing techniques. Furthermore, it reduces the computation time of the standard Particle Filter by 59.2%. The novel Particle Filter–H∞ Filter met the desired attitude estimation accuracy requirements of ±0.1° for an EO mission and showed the best performance in the simulation cases of non-Gaussian noise, magnetometer-only attitude estimation, measurement outliers, coloured noise and 6U CubeSat, demonstrating the robustness of the novel method in cases where the other attitude estimators do not provide the desired attitude estimation performance. For the case of Gaussian noise, the novel Particle Filter–H∞ Filter achieved a steady-state attitude accuracy of ±0.0526° for the roll axis, ±0.0360° for the pitch axis, and ±0.1005° for the yaw axis. Thus, this novel solution presents an advanced estimation algorithm that addresses the need for higher accuracy in small satellite attitude determination and robust performance that can be used in various scenarios of satellite operation and space environment noise. Some recommendations for future research include expanding the implementation of the attitude estimator to include other attitude sensors and further evaluating the estimator performance with an additional attitude control loop

The development and performance evaluation of Particle Filter–H∞ Filter for attitude estimation of a small satellite in low earth orbit

The accurate determination of the attitude of small satellites is essential for missions with stringent pointing requirements. Small satellites such as CubeSats are subject to mass and volume constraints, which directly affects their ability to carry more extensive and demanding payloads and limits their power and computational capabilities. There is, therefore, a need to minimise the computational cost of attitude determination while maximising accuracy and robustness to make small satellites useful for a wide range of applications. This research investigates the use of low-cost commercial sensors for attitude determination, namely the three-axis magnetometer and sun sensors, and proposes a novel hybrid Particle Filter–H∞ Filter to facilitate higher performance in the attitude determination of CubeSats. Existing attitude determination methods based on Kalman Filters are often not robust enough, which reduces their estimation accuracy, and they are limited in their inability to handle non-Gaussian noise. While Particle Filters offer higher estimation accuracy and the ability to handle non-Gaussian noise, their high computational cost is a distinct disadvantage. This research focuses on the development of a novel attitude estimator, the Particle Filter–H∞ Filter, which aims to achieve high estimation accuracy, robustness and lower computational cost by incorporating an H∞ Filter into the resampling step of a Particle Filter. The novel Particle Filter–H∞ Filter was developed and executed in a MATLAB simulation environment, in which an attitude determination system combining the relevant satellite environment models (the Simplified General Perturbations 4 orbit propagation model, the International Geomagnetic Reference Field model, the Sun position model), as well as three-axis magnetometer and sun sensor models and Euler’s equations for rigid body dynamics, including different simulation scenarios for a 3U CubeSat in low Earth orbit, was examined to verify the novel estimator. The simulation scenarios involve evaluating the estimator’s performance under different noise assumptions: Gaussian, non-Gaussian, increased process noise and coloured noise. Also, other simulation scenarios considered are magnetometer-only attitude estimation, large initial errors (observed during satellite separation and detumbling phase), the presence of measurement outliers and attitude estimation in a 6U CubeSat platform. The performance of the novel Particle Filter–H∞ Filter was evaluated against widely used nonlinear attitude estimators, namely the Extended Kalman Filter, the Unscented Kalman Filter, the H∞ Filter, and the standard Particle Filter, to determine its superiority with respect to the selected performance criteria: accuracy, robustness, computation time, and the ability to converge quickly and maintain convergence under different operating conditions. Compared to Particle Filters, the Kalman Filter-based attitude estimators are less computationally intensive, but their estimation accuracy is relatively poor. The results of various simulated scenarios have shown that the novel Particle Filter–H∞ Filter significantly improves the attitude estimation accuracy and has higher robustness compared to existing techniques. Furthermore, it reduces the computation time of the standard Particle Filter by 59.2%. The novel Particle Filter–H∞ Filter met the desired attitude estimation accuracy requirements of ±0.1° for an EO mission and showed the best performance in the simulation cases of non-Gaussian noise, magnetometer-only attitude estimation, measurement outliers, coloured noise and 6U CubeSat, demonstrating the robustness of the novel method in cases where the other attitude estimators do not provide the desired attitude estimation performance. For the case of Gaussian noise, the novel Particle Filter–H∞ Filter achieved a steady-state attitude accuracy of ±0.0526° for the roll axis, ±0.0360° for the pitch axis, and ±0.1005° for the yaw axis. Thus, this novel solution presents an advanced estimation algorithm that addresses the need for higher accuracy in small satellite attitude determination and robust performance that can be used in various scenarios of satellite operation and space environment noise. Some recommendations for future research include expanding the implementation of the attitude estimator to include other attitude sensors and further evaluating the estimator performance with an additional attitude control loop