Nanosatellite Attitude Estimation from Vector Measurements Using SVD-Aided UKF Algorithm

Çilden, Demet; Söken, Halil Ersin; Hajiyev, Chingiz

doi:10.1515/mms-2017-0011

Cited by 19 publications

(11 citation statements)

References 17 publications

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“…In a previous study, Cilden et al 42 improved the numerical computation stability of the UKF by adopting singular value decomposition (SVD) instead of Cholesky decomposition. Although this method could ensure the positive definiteness of the covariance matrix in the filtering process, the results obtained by SVD only formally meet the requirements of the square root, and there is no obvious improvement in the accuracy of the covariance matrix in the filtering process.…”

Section: Model and Methodsmentioning

confidence: 99%

Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering

Lian

Wang

et al. 2022

Intl J of Energy Research

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Summary Obtaining an accurate mapping relationship between the state‐of‐charge (SOC) and open‐circuit voltage (OCV) of lithium‐ion batteries at different ambient temperatures is of great significance for realizing accurate lithium‐ion battery SOC estimation considering the ambient temperature influence. However, the desired OCV‐SOC relationship is highly nonlinear, and the conventional polynomial fitting method is likely to result in relatively large fitting errors. To solve this problem, a method based on a backpropagation neural network (BPNN) to improve the OCV‐SOC fitting accuracy is proposed, and the SOC estimation of lithium‐ion batteries considering ambient temperature influence is completed. First, the relationship between the SOC and OCV of a lithium‐ion battery at different ambient temperatures is obtained by establishing a BPNN fitting model. Second, by optimizing the covariance decomposition process, a diagonalization of matrix unscented Kalman filtering (UKF) is proposed, which improves the accuracy and stability of the filtering algorithm. Then, the forgetting factor recursive least squares algorithm is combined to accomplish the online update of battery model parameters. Finally, under three working conditions, the effectiveness and robustness of the proposed method are verified. The simulation results show that the proposed method can obtain the most accurate SOC estimation results at each temperature, and the root mean square error (RMSE) and mean absolute error (MAE) under all three working conditions are less than 1.1%. Even if there is a certain error in the initial SOC, the proposed method can ensure that the RMSE and MAE of the SOC estimation results at each temperature do not exceed 1.5%.

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Section: Model and Methodsmentioning

confidence: 99%

Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering

Lian

Wang

et al. 2022

Intl J of Energy Research

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“…If the state vector is arranged as (17) and the mathematical model of the LEO satellite's rotational motion about its centre of mass is linearized using quasi-linearization method, we can consider a real-time linear Taylor approximation of the system function at the previous state estimate. The Kalman Filter obtained will be called the Extended Kalman Filter (EKF).…”

Section: Ekf Design For Attitude and Rate Estimationmentioning

confidence: 99%

“…The authors considered the non‐diagonal elements of the covariance matrix of the SVD to be small compared to the diagonal elements. Therefore, the non‐diagonal elements are neglected, and the error covariance matrix considered diagonally . In this study, an SVD‐aided EKF method using UD factorization is considered as an extended version of .…”

Section: Introductionmentioning

confidence: 99%

SVD‐aided EKF attitude estimation with UD factorized measurement noise covariance

Cilden–Guler

Hajiyev

2018

Asian Journal of Control

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This paper describes singular value decomposition (SVD) aided extended Kalman filter (EKF) for nanosatellite's attitude estimation. The development of the filter kinematic/dynamic model, the measurement models of the sun sensors, and the magnetometers used to generate vector measurements are presented. Vector measurements are used in SVD for satellite attitude determination purposes. In the proposed method, EKF inputs come from SVD as the linear measurements of attitude angles and their error covariance. In this step, UD factorizes the attitude angles error covariance, forming the measurements in order to obtain the appropriate inputs for the filtering stage. Results are presented and analyzed in addition to discussion of the sub-step, which is the UD factorization on the measurement covariance. The accuracy of the estimation results of the SVD-aided EKF with and without UD factorization is compared for estimation performance.

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“…This makes reasonable the choice for using it in conjunction with the Euler angles coordinate system and the shifted frame of reference method of [19]. An attempt towards this direction can be found in [14]. On the other hand, when the quaternion representation is used, the UKF in a standard format cannot be implemented straightforwardly.…”

Section: Introductionmentioning

confidence: 99%

A critical comparison on attitude estimation: From gaussian approximate filters to coordinate‐free dual optimal control

Koumpis

Panagiotou

Arvanitakis

2021

IET Control Theory & Appl

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This paper conveys attitude and rate estimation without rate sensors by performing a critical comparison, validated by extensive simulations. The two dominant approaches to facilitate attitude estimation are based on stochastic and set-membership reasoning. The first one mostly utilizes the commonly known Gaussian-approximate filters, namely the EKF and UKF. Although more conservative, the latter seems to be more promising as it considers the inherent geometric characteristics of the underline compact state space and accounts-from first principles-for large model errors. The set-theoretic approach from a control point of view is addressed, and it is shown that it can overcome reported deficiencies of the Bayesian architectures related to this problem, leading to coordinate-free optimal filters. Lastly, as an example, a modified predictive filter is derived on the tangent bundle of the special orthogonal group(3). INTRODUCTIONAttitude and rate estimation is an important aspect of aerial robotics. Throughout the decades, it has proven very accurate and versatile in applications from the first Low Earth Orbit (LEO) satellites [1] to unmanned aerial vehicles (UAVs) [2] and from the unmanned aerial systems [3] to recent aerial robotic workers [4]. At the same time, technological and technical advances allow for increased specifications of autonomy in conjunction with precise and agile manoeuvring. Consequently, position and orientation (attitude) control constitutes a field of research that is vital component of aerial robotics. In many cases, the model can be decoupled and attitude control can be implemented independently from position control [2]. Lately, more focus has been given to attitude controllers due to the increased difficulty and complexity of the specific control problem [5]; the success of these controllers relies upon the accurate knowledge of the real orientation and the angular rate of the aerial robot. Thus, it is imperative to develop efficient attitude filters, to deal not only with the measurement noise but also with the model errors.When a-priori statistical information is available, such uncertainties are represented by utilisation of the stochastic framework. Subsequently, model errors and measurement noise areThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Nanosatellite Attitude Estimation from Vector Measurements Using SVD-Aided UKF Algorithm

Cited by 19 publications

References 17 publications

Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering

Considering the temperature influence state‐of‐charge estimation for lithium‐ion batteries based on a back propagation neural network and improved unscented Kalman filtering

SVD‐aided EKF attitude estimation with UD factorized measurement noise covariance

A critical comparison on attitude estimation: From gaussian approximate filters to coordinate‐free dual optimal control

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