• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1095-1098
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• 1996

In this work, attitude determination with Inertial Reference Unit as attitude sensor is considered. Usually, the attitude error from IRU increases because of gyro rate bias and noise. Therefore, other attitude sensors(sun sensor, horizon sensor, star tracker) are needed to compensate for error from IRU. In this paper, we use the extended Kalman filter for attitude estimation of spacecraft with IRU and star tracker.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.80-86
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• 2010

We propose a spacecraft attitude estimation algorithm using a federated unscented Kalman filter. For nonlinear spacecraft systems, the unscented Kalman filter provides better performance than the extended Kalman filter. Also, the decentralized scheme in the federated configuration makes a robust system because a sensor fault can be easily detected and isolated by the fault detection and isolation algorithm through a sensitivity factor. Using the proposed algorithm, the spacecraft can continuously perform a given mission despite navigation sensor faults. Numerical simulation is performed to verify the performance of the proposed attitude estimation algorithm.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.214-221
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• 2016

An attitude estimation algorithm which integrates gyroscope and vision measurements using an adaptive complementary filter is proposed in this paper. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, fuzzy interpolator is applied. For recognizing the dynamic condition of the system and vision measurement fault, the cut-off frequency of the complementary filter is determined adaptively by using the fuzzy logic with designed membership functions. The performance of the proposed algorithm is evaluated by experiments and it is confirmed that proposed algorithm works well in the static or dynamic condition.

• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.36 no.7
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• pp.680-687
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• 2008

This paper presents map creation algorithm and initial attitude estimation method for the proposed optical head tracker system. The optical head tracker system consists of the IR stereo cameras and infrared LEDs as features on the helmet. In order for the stereo camera to track the luminous LEDs, it must take in to account the light radiation from the LEDs to determine the position of the center points. The proposed map creation algorithm makes map data about the position of features center points on the helmet frame. Also, initial attitude estimation method is proposed to estimate the initial attitude and position of a pilot head from the camera frame by the use of the feature pattern on the helmet. Therefore, the head motion can be expressed with respect to the body frame of a flight.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.1
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• pp.265-274
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• 2019

The paper describes an application of unscented Kalman filter(UKF) for attitude estimation of an unmanned vehicle(UV), which is equipped with a low-cost attitude heading reference system (AHRS). The roll, pitch and yaw required at the correction stage of the UKF are calculated from the measurements of acceleration and geomagnetic field. The roll and pitch are attributed to the measurement of acceleration, while yaw is calculated from the geomagnetic field measurement. Since the measurement of geomagnetic field is vulnerable to distortion by hard-iron and soft-iron effects, the calculated yaw has more uncertainty than the calculated roll and pitch. To reduce the uncertainty of geomagnetic field measurement, the proposed method estimates bias in the geomagnetic field measurement and compensates for the bias for more accurate calculation of yaw. The proposed method is verified through navigation experiments of a UV in a test pool. The results show that the proposed method yields more accurate attitude estimation; thus, it results more accurate location estimation.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.546-551
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• 2011

Tilt sensor is required to control the attitude of the biped robot when it walks on an uneven terrain. Vision sensor, which is used for recognizing human or detecting obstacles, can be used as a tilt angle sensor by comparing current image and reference image. However, vision sensor alone has a lot of technological limitations to control biped robot such as low sampling frequency and estimation time delay. In order to verify limitations of vision sensor, experimental setup of an inverted pendulum, which represents pitch motion of the walking or running robot, is used and it is proved that only vision sensor cannot control an inverted pendulum mainly because of the time delay. In this paper, to overcome limitations of vision sensor, Kalman filter for the multi-rate sensor fusion algorithm is applied with low-quality gyro sensor. It solves limitations of the vision sensor as well as eliminates drift of gyro sensor. Through the experiment of an inverted pendulum control, it is found that the tilt estimation performance of fusion sensor is greatly improved enough to control the attitude of an inverted pendulum.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2092-2095
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• 2001

In this paper, the performance of a new alternative form of three-axis attitude estimation algorithm for a rigid body is evaluated via simulation for the situation where the observed vectors are the estimated baselines of a GPS antenna array. This method is derived based on a simple iterative nonlinear least-squares with four elements of quaternion parameter. The representation of quaternion parameters for three-axis attitude of a rigid body is free from singularity problem. The performance of the proposed algorithm is compared with other eight existing methods, such as, Transformation Method (TM), Vector Observation Method (VOM), TRIAD algorithm, two versions of QUaternion ESTimator (QUEST), Singular Value Decomposition (SVD) method, Fast Optimal Attitude Matrix (FOAM), Slower Optimal Matrix Algorithm (SOMA).

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1352-1355
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• 1997

This paper presents the algorithm for estimating the attitude of an underwater vehicle using EFK. The system model is designed by linerizing the nonlinear Euler angle differential equation and the measurements is a speed logger output. The simulation result shows that the estimation lagorithm is adequate for decreasing attitude errors that grow abruptly during the motion with acceleration and rotation. It also shows that we can adapt the algorithm for compensating initial attitude errors generated after initial leveling.