Quaternion Invariant Extended Kalman Filtering for Spacecraft Attitude Estimation

Gui, Haichao; Ruiter, Anton H. J. de

doi:10.2514/1.g003177

Cited by 29 publications

(24 citation statements)

References 26 publications

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“…Remark 2. The proposed H∞ filter ( 34) is similar to the quaternion MEKF [29] and the quaternion left-invariant EKF (LIEKF) [30]. More precisely, suppose that in the kinematic system under consideration (3), the process disturbance δ and the measurement errors {εi} are zeromean Gaussian noises with standard deviations σω and {σi}, respectively.…”

Section: Gain Update Equationmentioning

confidence: 99%

Nonlinear $H_{\infty}$ Filtering on the Special Orthogonal Group $SO(3)$ using Vector Directions

Aslam¹,

Haydar²

2022

Preprint

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Section: Gain Update Equationmentioning

confidence: 99%

Nonlinear $H_{\infty}$ Filtering on the Special Orthogonal Group $SO(3)$ using Vector Directions

Aslam¹,

Haydar²

2022

Preprint

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“…Non-linear Kalman filter was used to integrate inertial and vision data in EKF [15], UKF [16], and CKF [17], [18]. The EKF [15] modeled the system by the first-order linearization. Thus, the EKF provided poor performance for non-linear dynamic systems [19].…”

Section: Introductionmentioning

confidence: 99%

Multi-Frequency Data Fusion for Attitude Estimation Based on Multi-Layer Perception and Cubature Kalman Filter

et al. 2020

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This paper proposes multi-frequency inertial and visual data fusion for attitude estimation. The proposed strategy is based on the locally weighted linear regression (LWLR), multi-layer perception (MLP), and cubature Kalman filter (CKF). First, we analyze the discrepant-frequency and the attitude divergence problems. Second, we construct the filter equation for the visual and inertial data and attitude differential equation for inertial-only data, which are used to estimate the attitude in time series. Third, we employ LWLR to compute the vision discrepancies between actual vision data and fitted vision data. The vision discrepancy is used as the input of MLP training. In MLP, the discrepancy is used as weights of the sums through the activation function of the hidden layer. To address the divergence problem, which is inherent in a multifrequency fusion, the MLP is utilized to compensate for the inertial-only data. Finally, experimental results on different environments of pseudo-physical simulations show the superior performance of the proposed method in terms of the accuracy of attitude estimation and divergence capability.

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“…Attitude estimation of a rigid body is an indispensable part of navigation. Questions of estimating attitude have been a field of concern for decades due to its numerous applications in various systems, such as unmanned underwater vehicles (UUVs) [ 1 ], unmanned aerial vehicles (UAVs) [ 2 ], and others [ 3 ]. A rigid body’s attitude can be resolved by integrating the angular velocity from a rate gyro output.…”

Section: Introductionmentioning

confidence: 99%

A Global Interconnected Observer for Attitude and Gyro Bias Estimation with Vector Measurements

Guo

Liu

et al. 2020

Sensors

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This paper proposes a novel interconnected observer to get good estimates of attitude and gyro bias from high-noise vector measurements. The observer is derived based on the theory of nonlinear and linear cascade systems, and its error dynamics have the properties of global exponential stability and robustness to bounded noise. These properties ensure the convergence and boundedness of the attitude and gyro bias estimation errors. To obtain higher estimation accuracy, an approach to calculate time-varying gains for the proposed auxiliary observer is designed under the premise of considering noise terms in the rate gyro and vector sensors. The simulation results show that when the vector sensors’ outputs contain high-level noise, the proposed observer with time-varying gains yields better performance in both the transient and steady-state phases.

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Quaternion Invariant Extended Kalman Filtering for Spacecraft Attitude Estimation

Cited by 29 publications

References 26 publications

Nonlinear $H_{\infty}$ Filtering on the Special Orthogonal Group $SO(3)$ using Vector Directions

Nonlinear $H_{\infty}$ Filtering on the Special Orthogonal Group $SO(3)$ using Vector Directions

Multi-Frequency Data Fusion for Attitude Estimation Based on Multi-Layer Perception and Cubature Kalman Filter

A Global Interconnected Observer for Attitude and Gyro Bias Estimation with Vector Measurements

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