Adaptive Tuning of the Unscented Kalman Filter for Satellite Attitude Estimation

Determining the process noise covariance of the unscented Kalman filter (UKF) is a difficult procedure. The analytical approximation method gives satisfactory results in certain cases, but it fails when generalized for the estimation of the extended states, such as the case that sensor biases or scale factors are included in the state vector. The main aim of this research is to find an appropriate tuning algorithm for the process noise covariance of the UKF when the magnetometer biases are estimated, as well as attitude and gyro biases. In this sense, an adaptive tuning method for an UKF that is used for satellite attitude estimation is given and the adaptive UKF algorithm is tested in various scenarios for the attitude and sensor bias estimation. The given adaptation method is an easy way of tuning the filter, especially in the absence of any analytical approximation for the calculation of the process noise covariance, and the performed simulations show that by using the adaptive UKF, it is possible to get accurate estimates that are close to optimal. (C) 2014 American Society of Civil Engineers.


Adaptive Fading UKF with Q-Adaptation: Application to Picosatellite Attitude Estimation
Söken, Halil Ersin (American Society of Civil Engineers (ASCE), 2013-07-01)
The unscented Kalman filter (UKF) is a filtering algorithm that gives sufficiently good estimation results for estimation problems of nonlinear systems even when high nonlinearity is in question. However, in the case of system uncertainty the UKF becomes inaccurate and diverges in time. In other words, if any change occurs in the process noise covariance, which is known a priori, the filter fails. This study introduces a novel adaptive fading UKF algorithm based on the correction of process noise covariance...
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Hacızade, Cengiz; Söken, Halil Ersin; Cilden-Guler, Demet (American Society of Civil Engineers (ASCE), 2019-09-01)
This study discusses simultaneous adaptation of the process and measurement noise covariance matrixes for a nontraditional attitude filtering algorithm. The nontraditional attitude filtering algorithm integrates the singular value decomposition (SVD) method with the unscented Kalman filter (UKF) to estimate the attitude of a nanosatellite. The SVD method uses magnetometer and Sun sensor measurements as the first stage of the algorithm and estimates the attitude of the nanosatellite, giving one estimate at a...
Estimation of pico-satellite attitude dynamics and external torques via Unscented Kalman Filter
Söken, Halil Ersin (FapUNIFESP (SciELO), 2014-01-01)
In this study, an Unscented Kalman Filter (UKF) algorithm is designed for estimating the attitude of a picosatellite and the in-orbit external disturbance torques. The estimation vector is formed by the satellite's attitude, angular rates, and the unknown constant components of the external disturbance torques acting on the satellite. The gravity gradient torque, residual magnetic moment, sun radiation pressure and aerodynamic drag are all included in the estimated external disturbance torque vector. The sa...
Contact mechanics problem between an orthotropic graded coating and a rigid punch of an arbitrary profile
ARSLAN, ONUR; Dağ, Serkan (Elsevier BV, 2018-01-01)
Singular integral equation (SIE) and finite element methods are developed for sliding contact analysis of a finite thickness orthotropic graded coating, which is perfectly bonded to an isotropic substrate. Orthotropic stiffness coefficients of the coating vary exponentially through the coating thickness. The coating is assumed to be loaded by a frictional rigid punch of an arbitrary profile. In the SIE formulation, governing partial differential equations are derived in accordance with the theory of plane e...
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Hacızade, Cengiz; Söken, Halil Ersin (American Society of Civil Engineers (ASCE), 2012-01-01)
This study introduces a robust Kalman filter (RKF) with a filter-gain correction for cases of measurement malfunctions. Using defined variables called measurement-noise scale factors, the faulty measurements are taken into consideration with a small weight and the estimations are corrected without affecting the characteristics of the accurate ones. In this study, RKF algorithms with single and multiple scale factors are proposed and applied for the state estimation process of an unmanned aerial vehicle (UAV...
Citation Formats
H. E. Söken, “Adaptive Tuning of the Unscented Kalman Filter for Satellite Attitude Estimation,” JOURNAL OF AEROSPACE ENGINEERING, pp. 0–0, 2015, Accessed: 00, 2020. [Online]. Available: