Q-Method Extended Kalman Filter

Metrology and Measurement Systems

2017

The integrated Singular Value Decomposition (SVD) and Unscented Kalman Filter (UKF) method can recursively estimate the attitude and attitude rates of a nanosatellite. At first, Wahba's loss function is minimized using the SVD and the optimal attitude angles are determined on the basis of the magnetometer and Sun sensor measurements. Then, the UKF makes use of the SVD's attitude estimates as measurement results and provides more accurate attitude information as well as the attitude rate estimates. The elements of "Rotation angle error covariance matrix" calculated for the SVD estimations are used in the UKF as the measurement noise covariance values. The algorithm is compared with the SVD and UKF only methods for estimating the attitude from vector measurements. Possible algorithm switching ideas are discussed especially for the eclipse period, when the Sun sensor measurements are not available.

Section: Svd Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Çilden

Söken

Metrology and Measurement Systems

2017

“…Equation (10) shows that trace of the product of transformation matrix A and transpose of the defined matrix B in (9) should be maximized with using statistical methods where l 0 = P a i . In this study, singular value decomposition (SVD) method is chosen to minimize the loss function problem as the optimal statistical method [18,19].…”

Section: Attitude Matrixmentioning

confidence: 99%

“…Their results can be used as inputs for either beginning of the filter or all the time. Integrated singleframe and Kalman filter algorithm is a non-traditional method because it uses the linear measurements rather than the nonlinear ones [10][11][12][13]. Also, covariance matrix can be determined for each step of the SVD and the trend of their results for each axis follows the line of the absolute angle errors.…”

Section: Introductionmentioning

confidence: 99%

Singular value decomposition based satellite attitude determination using different sensor configurations

Cilden–Guler

Int. J. Metrol. Qual. Eng.

2017

Abstract. Satellite's attitude can be determined by using several different ways. In order to use a recursive filter to determine the attitude especially after crucial intervals for the satellite including detumbling, eclipse, etc., single-frame methods can be used, and singular value decomposition (SVD) method has been selected because of its robustness for this paper. They can be placed for the initial values, or continuous input to the filter in addition to covariance matrix at each step. Also, sensor configurations depending on the position of the Sun are considered and used in the SVD method. Wahba's loss function is minimized with separating the basic matrix including the reference and model vectors into singular values because many researches show the SVD as the most robust algorithm after error analyses between several single-frame methods. Sun sensor, magnetometer, and horizon sensor are the selected common attitude sensors for small satellites in order to compare their attitude determination performances. For this purpose, absolute errors for different sensor configurations in the simulation environment in addition to the RMS errors are presented. The purpose of the paper is to present an attitude determination concept for a nanosatellite. Also, different sensor configurations are considered in the single-frame method with switching logic depending on the vector data. Horizon sensor and sun sensor are the optimum pair for the light side of the Earth on the other hand, during the eclipse; horizon sensor and magnetometer give the most accurate results. Study shows that the proposed algorithm can be performed for initial values to the Kalman filter, and in the transient phase before the filter tuned in practical satellite usage. In the eclipse period, sun-independent sensors are used and changing configurations provide better attitude estimation results to filtering techniques.

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

Cilden–Guler

Asian Journal of Control

2018

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.