Satellite attitude estimation using SVD-Aided EKF with simultaneous process and measurement covariance adaptation

Hajiyev, Chingiz; Cilden–Guler, Demet

doi:10.1016/j.asr.2021.07.006

Cited by 15 publications

(5 citation statements)

References 38 publications

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“…State and parameter estimation of nonlinear systems plays a significant role in the real time control and signal processing applications like ballistic object tracking, aircraft tracking [1], under water target tracking [2], satellite attitude estimation [3], navigation systems [4], state-of-charge (SOC) estimation [5] for battery management unit of electric vehicles, friction coefficient estimation for designing anti-lock braking systems [6], and many more. Since the introduction of Kalman filter (KF) as a state estimator for stochastic systems by R.E.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Poluri

Dey

2022

Asian Journal of Control

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This paper proposes new algorithms of adaptive Gaussian filters for nonlinear state estimation with maximum one‐step randomly delayed measurements. The unknown random delay is modeled as a Bernoulli random variable with the latency probability known a priori. However, a contingent situation has been considered in this work when the measurement noise statistics remain partially unknown. Due to unavailability of the complete knowledge of measurement noise statistics, the unknown measurement noise covariance matrix is estimated along with states following: (i) variational Bayesian approach, (ii) maximum likelihood estimation. The adaptation algorithms are mathematically derived following both of the above approaches. Subsequently, a general framework for adaptive Gaussian filter is presented with which variants of adaptive nonlinear filters can be formulated using different rules of numerical approximation for Gaussian integrals. This paper presents a few of such filters, viz., adaptive cubature Kalman filter, adaptive cubature quadrature Kalman filter with their higher degree variants, adaptive unscented Kalman filter, and adaptive Gauss–Hermite filter, and demonstrates the comparative performance analysis with the help of a nontrivial Bearing only tracking problem in simulation. Additionally, the paper carries out relative performance comparison between maximum likelihood estimation and variational Bayesian approaches for adaptation using Monte Carlo simulation. The proposed algorithms are also validated with the help of an off‐line harmonics estimation problem with real data.

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Section: Introductionmentioning

confidence: 99%

“…During attitude estimation also the adaptive KFs are necessitated. In a recent work [3], the satellite attitude is estimated using adaptive EKF with magnetometer and sun sensors where sensors covariance needs adaptation. In vision-based target tracking, requirement of adaptive of noise statistics is felt.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Poluri

Dey

2022

Asian Journal of Control

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“…The SVD-aided EKF reduces the computational cost of the EKF by using rough attitude estimates from the SVD algorithm instead of non-linear measurements [24]. The estimation covariance obtained from the SVD is used as the measurement noise covariance matrix in the filter, which inherently makes it adaptive to noise increments in measurements.…”

Section: Svd-aided Ekf Algorithmmentioning

confidence: 99%

“…In response, further research has been conducted to develop improved approaches that can make the EKF more adaptable to anomalies such as measurement faults during extended eclipse periods. In [24], the authors present a technique for computing satellite attitude estimation using an EKF with singular value decomposition (SVD) assistance. The algorithm also incorporates simultaneous adjustments of the process and measurement covariance utilizing data obtained from magnetometers and sun sensors.…”

Section: Introductionmentioning

confidence: 99%

Attitude Determination System for a Cubesat Experiencing Eclipse

Mmopelwa,

Ramodimo,

Matsebe

et al. 2023

Sensors

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In the context of Kalman filters, the predicted error covariance matrix Pk+1 and measurement noise covariance matrix R are used to represent the uncertainty of state variables and measurement noise, respectively. However, in real-world situations, these matrices may vary with time due to measurement faults. To address this issue in CubeSat attitude estimation, an adaptive extended Kalman filter has been proposed that can dynamically estimate the predicted error covariance matrix and measurement noise covariance matrix using an expectation-maximization approach. Simulation experiments have shown that this algorithm outperforms existing methods in terms of attitude estimation accuracy, particularly in sunlit and shadowed phases of the orbit, with the same filtering parameters and initial conditions.

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“…Then, such information can be applied in stochastic estimation schemes, such as the extended Kalman filter (EKF), which can further improve the results. For instance, the uncertainty of the attitude determination based on the Singular Value Decomposition (SVD) has recently been used to improve the accuracy and robustness of an implementation of the EKF [ 7 ]. Moreover, the covariance of an estimate can be used to establish the relative weight of the corresponding estimated value in sensor fusion, which combines estimates from different sources.…”

Section: Introductionmentioning

confidence: 99%

Attitude Uncertainty Analysis of a Three-Vehicle Constrained Formation

Cruz

Batista

2022

Sensors

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The uncertainty analysis of attitude estimates enables the comparison between different methods, and, thus, it is important for practical applications. This work studies the uncertainty for the attitude determination of a three-vehicle constrained formation. Moreover, the existing solution is improved by including the uncertainty results in a weighted orthogonal Procrustes problem. In the formation considered herein, the vehicles measure inertial references and relative line-of-sight vectors. Nonetheless, the line of sight between two elements of the formation is restricted. The uncertainty analysis uses perturbation theory and, consequently, considers a small first-order perturbation in the measurements. The covariance matrices are obtained for all relative and inertial attitude candidates from the linearization of the solution using a first-order Taylor expansion. Then, the uncertainty is completed by considering the covariance for the weighted orthogonal Procrustes problem, from the literature, and the definition of covariance for the remaining attitudes. The uncertainty characterization is valid for configurations with a unique solution. Finally, the theoretical results are validated by applying Monte Carlo simulations, which show that the predicted errors are statistically consistent with the numerical implementation of the solution with noise. Furthermore, the theoretical uncertainty predicts the accuracy changes near special configurations where there is loss of information.

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Satellite attitude estimation using SVD-Aided EKF with simultaneous process and measurement covariance adaptation

Cited by 15 publications

References 38 publications

Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Adaptive Gaussian filters for nonlinear state estimation with one‐step randomly delayed measurements

Attitude Determination System for a Cubesat Experiencing Eclipse

Attitude Uncertainty Analysis of a Three-Vehicle Constrained Formation

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