Regularized robust filter for attitude determination system with relative installation error of star trackers

Huang

IET Control Theory & Applications

et al. 2014

This study is concerned with the robust extended Kalman filtering problem for non-linear attitude estimation systems with multiplicative noises and unknown external disturbances. The multiplicative noises are modelled by random variables with bounded variance. The unknown external disturbances are described to lie in bounded set. The objective of the addressed attitude estimation problem is to design a filter such that, in the presence of both the multiplicative noises and unknown external disturbances, an optimised upper bound on the state estimation error variance can be guaranteed. Thus, a robust extended Kalman filter (REKF) is presented for attitude estimation with multiplicative noises and unknown external disturbances. Compared with the traditional extended Kalman filter in attitude estimation, the proposed algorithm takes into consideration the effects of multiplicative noises and unknown external disturbances. Moreover, the stability of the proposed REKF can be proved under certain conditions by utilising the stochastic stability theory. Finally, the simulation results demonstrate the effectiveness of the proposed REKF.

Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust extended Kalman filter for attitude estimation with multiplicative noises and unknown external disturbances

Huang

IET Control Theory & Applications

et al. 2014

Abstract and Applied Analysis

“…The measurement errors of sensors are inevitable in real applications, and the gyro and star sensor are no exception. Though the literature [14] takes into consideration the misalignment error of star sensors, no attention is paid to the misalignment errors and scale factor errors of the gyro. However, they can lead to the uncertainty process model.…”

Section: Uncertainty Measurement Model With Star Sensor Delaysmentioning

confidence: 99%

“…Among them, the robust Kalman filter design based on the minimum variance theory has been approved to be an effective methodology. Based on this, Wang et al [14] proposed a regularized robust filter for attitude determination system to deal with the installation error of star trackers. In this work, 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.…”

Section: Introductionmentioning

confidence: 99%

Finite-Horizon Robust Kalman Filter for Uncertain Attitude Estimation System with Star Sensor Measurement Delays

Qian¹,

Huang²,

Liu³

2014

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.

“…satellite applications such as high-resolution imaging and high-accuracy mapping [1], [2]. The performance of attitude determination is not only dependent on the performance of hardware configurations of the attitude sensors, but also associated with the adopted attitude filter algorithms [3].…”

Section: Introductionmentioning

confidence: 99%

Turbulence Error Modeling and Restriction for Satellite Attitude Determination System Based on Improved Maximum Correntropy Kalman Filter

et al. 2019

In the process of satellite attitude determination, satellites or sensors themselves often encounter a variety of turbulence influences due to the complexity of space environments. Such influences can lead to the mutation and non-Gaussian noises for the attitude determination system. To solve these problems, in this paper, we construct a unified error model for the turbulence influences, which is a non-Gaussian noise model, and propose an improved attitude filter method to restrict the turbulence noises and the system mutation to enhance attitude determination accuracy and robustness. The unified error model combined with jitters and vibrations in the actual process of satellite attitude determination is firstly designed. Then an Improved Adaptive Kalman filter (IAKF) based on both the Strong Tracking Filter (STF) and the Maximum Correntropy Filter (MCKF) is put forward. By using of the optimization principle with both of fading factor and Maximum Correntropy Criterion (MCC), this proposed filter algorithm can suppress the influences of system mutations and non-Gaussian noises at the same time. It can eliminate the system mutations and the turbulence errors, and achieve excellent robustness and the attitude determination accuracy for the nonlinear system. Extensive simulations of the proposed filter are conducted under the conditions of the Gaussian noises, system mutation with large outliers, non-Gaussian noise with turbulence noises, and both the mutation and non-Gaussian turbulence error. The results demonstrate that our filter outperforms the existing attitude filter algorithms significantly.