Symmetry preserving nonlinear observer for attitude estimation with magnetometer only

Sabzevari, Sanaz; Arvan, Mohammad Reza; Vali, Ahmad Reza; Dehghan, Seyyed Mohammad Mehdi; Ferdowsi, Mohammad Hossein

doi:10.1016/j.isatra.2020.03.005

Cited by 7 publications

(2 citation statements)

References 20 publications

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“…Kalman type filters are widely used attitude estimators, and they can estimate the attitude state through a weighted combination of attitude dynamic based prediction and observation sensor corrections according to the noise distribution properties (Sadaghzadeh et al, 2014; Wang et al, 2020). Recently, the Lie groups attitude system dynamics has been studied because exploiting the geometrical properties of Lie groups models can significantly simplify the estimation problem and improve the performance of guidance, navigation, and control in aerospace applications (Pan et al, 2021; Sabzevari et al, 2020). For example, invariant state observers and filters have been investigated especially for spacecraft attitude dynamics on the special orthogonal group SO(3) ; related research on the orbit determination and navigation systems has been conducted on the special Euclidean group SE (3) (Bonnabel et al, 2009; Vasconcelos et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Adaptive invariant Kalman filtering for lie groups attitude estimation with biased and heavy-tailed process noise

Wang

Zhang

Liu

et al. 2022

Transactions of the Institute of Measurement and Control

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Attitude determination is fundamental for spacecraft missions in aerospace engineering. Kalman filter (KF) is the optimal estimator in least square sense and, using the symmetry properties of matrix Lie groups system, the invariant Kalman filter (IKF) has been developed to boost the filtering performance for attitude estimation. However, due to presence of frequent and severer maneuvers, the Lie groups attitude dynamics is usually corrupted by significant biases and heavy-tailed outliers, which usually leads to decreased precision of IKF. For attitude estimation problem troubled by biased and heavy-tailed process noise, this work proposes a new invariant Kalman filter (VBAIKF) by constructing the hierarchical Gaussian system model: the probability density function of prior estimate state is first described using the student’s t distribution, while the unknown scale covariance matrix and degrees of freedom (dof) of the employed student’s t distribution are defined as the inverse Wishart distribution (IWD) and Gamma distribution. In VBAIKF, the Lie groups rotation matrix of spacecraft, the biased mean, the parameters of dof and scale covariance matrix are online estimated together by variational Bayesian fixed-point iterations. The simulation results with Lie groups attitude estimation system further verify the superior filtering adaptability and precision of proposed approach VBAIKF than other methods for attitude determination with biased mean and heavy-tailed process noise.

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Section: Introductionmentioning

confidence: 99%

Adaptive invariant Kalman filtering for lie groups attitude estimation with biased and heavy-tailed process noise

Wang

Zhang

Liu

et al. 2022

Transactions of the Institute of Measurement and Control

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“…n addition, for only using the magnetometer to obtain the angular rate and other attitude information, also through the establishment of the projectile dynamics model, Li et al introduces a kind of gyro angular rate estimation method [11], Natanson and others introduce a three-axis magnetometer measurement spacecraft attitude and angular rate RTSF (real-time) sequential filtering method, and Ma and Xu put forward improved real-time sequential filtering method (IRTSF) [12]. Sabzevari et al use the magnetometer [13,14] to estimate the attitude in vehicle dynamics model is established on the basis of calculating. However, in the process of high-speed flight, the force and torque, including aerodynamic force, subjected to the projectile are very complex; therefore, accurate dynamic model cannot be established.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time Estimation Method of Roll Angle and Angular Rate Based on Geomagnetic Information

Gao

Zhang

et al. 2020

Mathematical Problems in Engineering

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In the course of the guidance transformation of the rotating projectile, the accurate acquisition of the roll angle and roll angle rate is very important to the attitude determination and guidance control of the rotating projectile. However, due to the impact of high rotation and high overload of projectile, MEMS gyros have problems such as limited range, saturation, overload, and even performance degradation, which make the roll angle rate unable to be output normally. At the same time, because the MEMS gyro estimation of roll angle is in the form of angular rate integral, the roll angle cannot be estimated normally if the roll angle rate cannot be accurately obtained. In order to solve this problem, a real-time estimation of projectile roll angle and roll rate based on geomagnetic information under high dynamic and high overload conditions is presented. Firstly, according to the motion characteristics of the rotating projectile, the motion model of the projectile is established, and the roll angle and roll angle rate of the projectile are estimated by Kalman filtering algorithm under the conditions of high axial rotation and high overload. Considering the high dynamic characteristics of the rotating projectile, based on the Kalman filter, the algorithm of the forgetting filter with the forgetting factor is further adopted to estimate the roll angle and roll angle rate, so as to reduce the error caused by the estimation delay in the process of high-speed dynamic change. Simulation data and semiphysical test results show that the accuracy of roll angle estimated by this method reaches about 2° in semiphysical test, which is one time higher than that calculated by the system. In the semiphysical experiment, the accuracy of the estimated roll rotation rate reaches 5 °/s, which is more than 6 times higher than that obtained by direct derivation. In the high dynamic stage, compared with the pure Kalman filter, the accuracy of roll angle with forgetting factor estimation is improved by an order of magnitude, and the accuracy of roll angle rate is improved by 4 times, which meets the desired accuracy of rotating projectile.

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Observer-based controller for VTOL-UAVs tracking using direct Vision-Aided Inertial Navigation measurements

2023

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Symmetry preserving nonlinear observer for attitude estimation with magnetometer only

Cited by 7 publications

References 20 publications

Adaptive invariant Kalman filtering for lie groups attitude estimation with biased and heavy-tailed process noise

Adaptive invariant Kalman filtering for lie groups attitude estimation with biased and heavy-tailed process noise

A Real-Time Estimation Method of Roll Angle and Angular Rate Based on Geomagnetic Information

Observer-based controller for VTOL-UAVs tracking using direct Vision-Aided Inertial Navigation measurements

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