• Title/Summary/Keyword: spacecraft attitude estimation

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Spacecraft Moment of Inertial Estimation by Modified Rodrigues Parameters (Modified Rodrigues Parameter 기반의 인공위성 관성모멘트 추정 연구)

  • Bang, Hyo-Choong
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.38 no.3
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    • pp.243-248
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    • 2010
  • This study addresses spacecraft moment of inertial estimation approach using Modified Rodrigues Parameters(MRP). The MRP offer advantage by avoiding singularity in Kalman Filter design for attitude determination caused by the norm constraint of quaternion parameters. Meanwhile, MRP may suffer singularity for large angular displacement, so that we designed appropriate reference attitude motion for accurate estimation. The proposed approach is expected to provide stable error covariance update with accurate spacecraft mass property estimation results.

Design and Verification of Spacecraft Pose Estimation Algorithm using Deep Learning

  • Shinhye Moon;Sang-Young Park;Seunggwon Jeon;Dae-Eun Kang
    • Journal of Astronomy and Space Sciences
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    • v.41 no.2
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    • pp.61-78
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    • 2024
  • This study developed a real-time spacecraft pose estimation algorithm that combined a deep learning model and the least-squares method. Pose estimation in space is crucial for automatic rendezvous docking and inter-spacecraft communication. Owing to the difficulty in training deep learning models in space, we showed that actual experimental results could be predicted through software simulations on the ground. We integrated deep learning with nonlinear least squares (NLS) to predict the pose from a single spacecraft image in real time. We constructed a virtual environment capable of mass-producing synthetic images to train a deep learning model. This study proposed a method for training a deep learning model using pure synthetic images. Further, a visual-based real-time estimation system suitable for use in a flight testbed was constructed. Consequently, it was verified that the hardware experimental results could be predicted from software simulations with the same environment and relative distance. This study showed that a deep learning model trained using only synthetic images can be sufficiently applied to real images. Thus, this study proposed a real-time pose estimation software for automatic docking and demonstrated that the method constructed with only synthetic data was applicable in space.

Inertia Estimation of Spacecraft Based on Modified Law of Conservation of Angular Momentum

  • Kim, Dong-Hoon;Choi, Dae-Gyun;Oh, Hwa-Suk
    • Journal of Astronomy and Space Sciences
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    • v.27 no.4
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    • pp.353-357
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    • 2010
  • In general, the information of inertia properties is required to control a spacecraft. The inertia properties are changed by some activities such as consumption of propellant, deployment of solar panel, sloshing, etc. Extensive estimation methods have been investigated to obtain the precise inertia properties. The gyro-based attitude data including noise and bias needs to be compensated for improvement of attitude control accuracy. A modified estimation method based on the law of conservation of angular momentum is suggested to avoid inconvenience like filtering process for noise-effect compensation. The conventional method is modified and beforehand estimated moment of inertia is applied to improve estimation efficiency of product of inertia. The performance of the suggested method has been verified for the case of STSAT-3, Korea Science Technology Satellite.

Spacecraft Attitude Determination Study using Predictive Filter (Predictive Filter를 이용한 인공위성 자세결정 연구)

  • Choi , Yoon-Hyuk;Bang, Hyo-Choong
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.33 no.11
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    • pp.48-56
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    • 2005
  • Predictive filter theory proposed recently can be characterized by inherent advantages of estimating modelling error and overcoming the disadvantage of the Kalman filter theory. A one-step ahead error is minimized to produce optimized filter performance in the form of the predictive filter. The main advantage of this filter lies in the ability to estimate both state vector and system model error. In this paper, attitude estimation results based upon the predictive filter theory is addressed. Mathematical formulation for estimating bias signal is peformed by using the predictive filter theory, and attitude estimation based upon vector observation is presented. From the results of this study, the potential applicability of the predictive filter is highlighted.

Overview of Star Tracker Technology and Its Development Trends (별추적기의 기술개요와 개발동향)

  • Ju, Gwang-Hyeok;Lee, Sang-Ryool
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.38 no.3
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    • pp.300-308
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    • 2010
  • In order to accelerate the evolution of faster, better, cheaper spacecraft, it is evident that greatly enhanced general-purpose attitude determination methods are needed Currently, star tracker sensors based on charge coupled devices (CCD) or active pixel sensors(APS) enable one to obtain the best spacecraft attitude estimation among the existing sensors for attitude determination. In this paper, basic principles of star tracker technology are explained including major issues arising in design and development of star tracker. Also, an historical overview and worldwide survey associated with various star trackers from star scanner through microelectromechanical system(MEMS)-based star tracker is offered.

Preliminary Test of Adaptive Neuro-Fuzzy Inference System Controller for Spacecraft Attitude Control

  • Kim, Sung-Woo;Park, Sang-Young;Park, Chan-Deok
    • Journal of Astronomy and Space Sciences
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    • v.29 no.4
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    • pp.389-395
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    • 2012
  • The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS). An ANFIS produces a control signal for one of the three axes of a spacecraft's body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw) and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.

Sensor Alignment Calibration for PrecisionAttitude Determination of Spacecrafts

  • Lee, Il-Hyoung;Ryoo, Chang-Kyung;Bang, Hyo-choong;Tahk, Min-Jea;Lee, Sang-Ryool
    • International Journal of Aeronautical and Space Sciences
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    • v.5 no.1
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    • pp.83-93
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    • 2004
  • A new alignment calibration method of attitude sensors for the precisionattitude determination of a spacecraft based on the extended Kalman filter is proposed.The proposed method is divided into two steps connected in series: the gyro and thestar tracker calibration. For gyro calibration, alignment errors and scale factor errorsare estimated during the calibration maneuver under the assumption of a perfect startracker. Estimation of the alignment errors of the star trackers and compensation ofthe gyro calibration errors are then performed using the measurements includingpayload information. Performance of the proposed method are demonstrated bynumerical simulations.

Unscented Kalman Filtering for Spacecraft Attitude and Rate Determination Using Magnetometer

  • Kim, Sung-Woo;Park, Sang-Young;Abdelrahman, Mohammad;Choi, Kyu-Hong
    • Bulletin of the Korean Space Science Society
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    • 2008.10a
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    • pp.36.1-36.1
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    • 2008
  • An Unscented Kalman Filter(UKF) for estimation of attitude and rate of a spacecraft using only magnetometer vector measurement is presented. The dynamics used in the filter is nonlinear rotational equation which is augmented by the quaternion kinematics to construct a process model. The filter is designed for low Earth orbit satellite, so the disturbance torques include gravity-gradient torque, magnetic disturbance torque, and aerodynamic drag. The magnetometer measurements are simulated based on time-varying position of the spacecraft. The filter has been tested not only in the standby mode but also in the detumbling mode. To stabilize the attitude, linear PD controller is applied and the actuator is assumed to be thruster. A Monte-Carlo simulation has been done to guarantee the stability of the filter performance to the various initial conditions. The UKF performance is compared to that of EKF and it reveals that UKF outperforms EKF.

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Spacecraft Precision Attitude Determination using UVF Measurements

  • Lee, Hun-Gu;Yoon, Jae-Cheol;Shin, Dong-Seok
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1881-1886
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    • 2005
  • This paper proposes a novel approach of a precision attitude determination algorithm using UVF (Unit Vector Filter) measurements. The proposed method is superior to the conventional QUEST measurements based approaches because the estimation performance can be greatly enhanced by selecting brighter stars having better noise characteristics. The performance comparison with QUEST measurements is made to verify the usefulness of the proposed algorithm.

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