Abstract
This paper addresses the robust Kalman filtering problem for uncertain attitude estimation system with star sensor measurement delays. Combined with the misalignment errors and scale factor errors of gyros in the process model and the misalignment errors of star sensors in the measurement model, the uncertain attitude estimation model can be established, which indicates that uncertainties not only appear in the state and output matrices but also affect the statistic of the process noise. Meanwhile, the phenomenon of star sensor measurement delays is described by introducing Bernoulli random variables with different delay characteristics. The aim of the addressed attitude estimation problem is to design a filter such that, in the presence of model uncertainties and star sensors delays for the attitude estimation system, the optimized filter parameters can be obtained to minimize the upper bound on the estimation error covariance. Therefore, a finite-horizon robust Kalman filter is proposed to cope with this question. Compared with traditional attitude estimation algorithms, the designed robust filter takes into account the effects of star sensor measurement delays and model uncertainties. Simulation results illustrate the effectiveness of the developed robust filter.
Highlights
Attitude estimation has played an important role in many actual applications, such as aerospace, satellites, marine, and robots
Our aim is to find an upper bound on the estimation error covariance and design a finite-horizon robust filter for (30) to minimize the upper bound
The root-mean square error (RMSE) and accumulative RMSE (ARMSE) [30, 31] of the attitude are employed to describe the quality of the attitude estimation
Summary
Attitude estimation has played an important role in many actual applications, such as aerospace, satellites, marine, and robots. Kalman filter has been employed to solve the attitude estimation filtering problem [1,2,3]. Pittelkau [5, 6] develops the Kalman filtering technique to estimate the calibration parameters of gyro and star sensor. Even though misalignment calibration is accomplished, the measurement misalignment error of gyro and star sensor cannot be removed completely, which lead to model uncertainty. In the case that an exact uncertain model is established, the robust filtering technique can be used to deal with the filtering problem with model uncertainty. Based on Abstract and Applied Analysis this, Wang et al [14] proposed a regularized robust filter for attitude determination system to deal with the installation error of star trackers. The installation error of star trackers is expressed as model uncertainty in the measurement model, but the measurement misalignment error of gyros is not taken into account
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