No abstract

This paper deals with microsatellite attitude determination systems which are a combination of a main estimator with high-power, high-precision sensors for higher accuracy estimation and a redundant estimator with low-power, lower-precision sensors for backup. Measurement data from all sensors in the redundant estimator are fused by the unscented Kalman filter to provide estimated attitude and the gyro bias values. Besides the accuracy of attitude sensors, the accuracy of this estimator depends largely on the selection of the process and measurement noise covariance matrices. In this paper, a novel real-time tuning unscented Kalman filter for redundant attitude estimator is introduced to tune these matrices efficiently in each filter step. The tuning process uses the estimated attitude of the main estimator as an independent truth reference data to calculate the cost function which is minimized by a downhill simplex algorithm. In the scheme developed in this paper a fine-tuning process is used, which results in faster convergence speed and higher estimated accuracy of the redundant estimator. Another important feature of the developed filter is that a flexibly estimated accuracy and system power consumption can be archived by choosing the duration and repeat frequency of turn-on time of the main estimator.

This paper provides a new method for robust spacecraft attitude estimation in the presence of measurement biases. The proposed method is developed based on the separate-bias or two-state Kalman filter which was first introduced by Friedland. The separate-bias Kalman filter consists of two stages: the first stage, the ''bias-free'' filter, is based on the assumption that the bias is nonexistent; the second stage, the ''bias'' filter, is implemented to estimate bias vectors. The output of the first filter is then corrected with the output of the second filter. In this research, the authors propose a real-time tuning method for a parameter in the Kalman gain calculation process of the ''bias'' filter. The adaptive scale factor is optimized relying on the minimization of the cost function, which is calculated from the difference between the predicted and measurement values. The proposed filter has a faster convergence speed from large initial errors and an increased accuracy on unpredicted bias models than conventional methods. Moreover, to verify these advantages, the research also provides analyses and comparisons between the proposed method with conventional methods like the original separate-bias Kalman filter, unscented Kalman filter and extended Kalman filter in several numerical simulation scenarios for a microsatellite.

This paper introduces a new faults detection and diagnosis (FDD) subsystem for microsatellite attitude determination systems which use one three-dimensional rate gyro and two vector sensors. This FDD subsystem includes two filters for residual generations, hypothesis tests for fault detections and a reference logic table for fault isolations and fault recovery. This automatic FDD subsystem helps to enhance the automatic ability of attitude determination flight software and to reduce the dependence on ground support. The scheme developed in this paper resolves the problem of the heavy and complex calculations during residual generation parts and therefore the delay in isolation process is also reduced. The numerical simulations for TSUBAME, a demonstration microsatellite of Tokyo Institute of Technology, are conducted and analyzed to demonstrate the working of this FDD subsystem.