A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites

Le, Huy X.; Matunaga, Saburo

doi:10.1016/j.actaastro.2014.08.020

Cited by 23 publications

(23 citation statements)

References 9 publications

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“…On the other hand, an incorrect a priori knowledge of statistical noise may lead to performance degradation and practical divergence [16][17][18][19][20]. In addition, in UKF-SLAM, there are some numerical problems such as loss of positive definite of the state covariance matrix, which derives from rounding errors and may lead to filtering failure.…”

Section: Introductionmentioning

confidence: 96%

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Robust SLAM: SLAM base on $$\hbox {H}_{\infty }$$ H ∞ square root unscented Kalman filter

Havangi

2015

Nonlinear Dyn

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Simultaneous localization and mapping (SLAM) is concerned to be the key point to realize the real autonomy of mobile robot. Extended Kalman filter (EKF) is a popular choice to solve SLAM problem. However, EKF-SLAM is vulnerable to linearization errors, which can cause poor performance and inconsistency. UKF-SLAM is proposed to solve these problems. However, the conventional UKF-SLAM is based on the assumption that the system is exactly known, and their disturbances are stationary Gaussian noises with known statistics, which these assumptions might not hold in many real applications. In this work, in order to avoid these shortcomings and to increase the accuracy of the estimation, the robust SLAM (RSLAM) is introduced. RSLAM is based on H ∞ square root unscented Kalman filter which is applicable for nonlinear system with non-Gaussian noises. The proposed method does not require the knowledge of the noise distributions, and the noises can be non-Gaussian, so it is more flexible and has less limitation in real applications. In addition, the RSLAM has consistently increased numerical stability comparing to UKF-SLAM because all resulting covariance matrices are guaranteed to stay semipositive definite. To get more acceptable performance, the parameters of RSLAM (including the covariance process noise Q and the covariance measurement noise R) are tuned by using an adaptive neuro-fuzzy inference system. The superior performance of RSLAM over other UKF-SLAM is validated by Monte Carlo simulation. The results show improvement in the accuracy and consistency of the state estimates.

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Section: Introductionmentioning

confidence: 96%

“…However, UKF and consequently UKF-SLAM have good performances under the assumption that accurate system model and perfect knowledge of the noise statistics are known. In addition, the noises are white with Gaussian distribution [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Robust SLAM: SLAM base on $$\hbox {H}_{\infty }$$ H ∞ square root unscented Kalman filter

Havangi

2015

Nonlinear Dyn

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“…In this work, we analyze all diverse additive unscented filters (UFs; meaning UKFs and SRUKFs) for quaternionic systems proposed in the literature; considering essentially distinct algorithms, we can enumerate the following works …”

Section: Introductionmentioning

confidence: 99%

Unscented and square‐root unscented Kalman filters for quaternionic systems

Menegaz

Ishihara

2018

Intl J Robust & Nonlinear

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Proper construction of an unscented Kalman filter (UKF) for unit quaternionic systems is not straightforward due to the incompatibility between the algebraic properties of the unit quaternions and the common real vector space operations (additions and scalar multiplications) needed in the steps of a filter algorithm. This work studies, in detail, all UKFs and square-root UKFs for quaternionic systems proposed in the literature. First, we classify the algorithms according to the preservation of the unity norm of the quaternion variables. Second, we propose two new algorithms: the quaternionic additive unscented Kalman filter (QuAdUKF) and a square-root variant of it. The QuAdUKF encompasses all known UKFs for quaternionic systems of the literature preserving, in all steps, the norm of the unit quaternion variables. Besides, it can also yield new UKFs with this norm preservation property. The QuAdUKF's square-root variant has better properties in comparison with all the square-root UKFs for quaternionic systems of the literature. Numerical experiments for a spacecraft attitude estimation problem illustrate the theoretical results. KEYWORDS attitude estimation, square-root unscented Kalman filter (SRUKF), unit quaternion, unscented Kalman filter (UKF)In the control literature, the most common state space models are those where the underlying state, input, and output variables are real vectors, that is, elements that lie in a Euclidean vector space ℝ n . For bodies under two-dimensional or three-dimensional (3D) motions, these Euclidean models fit for dimensionless material points, that is, linear displacements and velocities. However, for large rigid bodies, besides these linear displacement characteristics, the body pointing direction and angular (rotational) movements become important. [1][2][3] It is well known that rotations of rigid bodies in a 3D space are mathematically represented by state trajectories, restricted to a compact manifold called the special orthogonal group, SO(3). The high nonlinearity of this manifold, due to the restriction of variables in specific regions, leads to difficulties in dealing with these variables. In estimation algorithms, performing calculations with variables in SO(3) is often computationally expensive, and more computationally-efficient rotation parameterizations were proposed in the literature. Among the alternative rotation parameterizations, such as Euler angles, rotation vectors (RoVs), and unit quaternions, the unit quaternions are often chosen because, unlike other 4500

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“…And then, combining the density function given in Equation (17), Equation (18) and Equation (19), under the maximization of ( ) J k , the k Q and k R given as:…”

Section: Map Criterionmentioning

confidence: 99%

“…In [19], an adaptive unscented Kalman filter algorithm with dynamic thresholds of covariance used to update measurement noise covariance matrix real-time was developed for satellite fault detection and diagnosis by the authors. As reported in [20], an adaptive unscented Kalman filter method with using adaptive error covariance matching technology to improve the state estimation accuracy was employed to estimate the battery energy and power capability by authors.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Adaptive Unscented Kalman Filter Algorithm

Chen

Zhang

et al. 2017

Preprint

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Abstract:In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.

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A residual based adaptive unscented Kalman filter for fault recovery in attitude determination system of microsatellites

Cited by 23 publications

References 9 publications

Robust SLAM: SLAM base on $$\hbox {H}_{\infty }$$ H ∞ square root unscented Kalman filter

Robust SLAM: SLAM base on $$\hbox {H}_{\infty }$$ H ∞ square root unscented Kalman filter

Unscented and square‐root unscented Kalman filters for quaternionic systems

A Hybrid Adaptive Unscented Kalman Filter Algorithm

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