• Journal of the Korean Society for Precision Engineering
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• v.15 no.7
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• pp.175-181
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• 1998

This paper describes realtime visual tracking system of moving object for three dimensional target using EKF(Extended Kalman Filter). We present a new realtime visual tracking using EKF algorithm and image prediction algorithm. We demonstrate the performance of these tracking algorithm through real experiment. The experimental results show the effectiveness of the EKF algorithm and image prediction algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.32 no.10
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• pp.367-375
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• 1983

An extended Kalman filter (EKF) algorithm for estimating aerodynamic parameters from missile flight data is evaluated using simulated test data. The algorithm includes a general purpose 6-DOF missile airframe suitable for representing a variety of missile configurations. The EKF is demonstrated to be well suited as a postflight analysis tool for extracting large numbers of airframe parameters from flight test measurements. A structure identification algorithm is evaluated using synthetic measurement data. This algorithm used in conjunction with the parameter identification algorithm, can select that model from a family of candidate models which most likely produced the synthetic measurement data.

• Journal of IKEEE
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• v.19 no.4
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• pp.557-565
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• 2015

In this paper, a method for estimating the location of a quadcopter is proposed by applying an EKF-SLAM algorithm to its flight control, to autonomously control the flight of an unmanned quadcopter. The usefulness of this method is validated through simulations. For autonomously flying the unmanned quadcopter, an algorithm is required to estimate its accurate location, and various approaches exist for this. Among them, SLAM, which has seldom been applied to the quadcopter flight control, was applied in this study to simulate a system that estimates flight trajectories of the quadcopter.

• Journal of Ocean Engineering and Technology
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• v.19 no.2 s.63
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• pp.82-89
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• 2005

This study deals with the development of the extended Kalman filter(EKF) algorithm for the localization of underwater mining vehicles. Both simulation and experimental studies in a test bed are carried out. For the experiments, a scale dawn tracked vehicle is run in a soil bin containing cohesive soil of bentonite-water mixture. To develop the EKF algorithm, we use a kinematic model including the inner/outer track slips and the slip angle for the vehicle. The measurements include the inner and outer wheel speeds from encoders, the heading angle from a compass sensor and a fiber optic rate gyro, and x and y coordinate position values from a vision system. The vision sensor replaces the LBL(Long Base Line) sonar system used in the real underwater positioning situations. Artificial noise signals mimicking the real LBL noise signal are added to the vision sensor information. To know the mean slip values of the tracks in both straight and cornering maneuver, several trial running experiments are executed before applying the EKF algorithm. Experimental results show the effectiveness of the EKF algorithm in rejecting the sensor measurements noise. Also, the simulation and experimental results show close correlations.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.4
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• pp.447-459
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• 2013

Many problems need to be solved before vision systems can actually be applied in industry, such as the precision of the kinematics model of the robot control algorithm based on visual information, active compensation of the camera's focal length and orientation during the movement of the robot, and understanding the mapping of the physical 3-D space into 2-D camera coordinates. An algorithm is proposed to enable robot to move actively even if the relative positions between the camera and the robot is unknown. To solve the correction problem, this study proposes vision system model with six camera parameters. To develop the robot vision control algorithm, the N-R and EKF methods are applied to the vision system model. Finally, the position accuracy and processing time of the two algorithms developed based based on the EKF and the N-R methods are compared experimentally by making the robot perform slender bar placement task.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.23-34
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• 2012

Space launch vehicles are designed to fly according to the elaborate pre-determined path. However, if a vehicle went out of the planned trajectory or its thrust terminated abnormally, or if a free-fall atmospheric reentry vehicle tracked by a tracking sensor became impossible to be measured, it is required to attempt to track by a another track equipment or estimate its impact point rapidly. In this paper a new algorithm is proposed, named the ITS-EKF combined with the Integrated Track Splitting (ITS) algorithm and the Extended Kalman Filter (EKF) to obtain the location information of a ballistic projectile without thrust, create its track and maintain it in an environment with clutter. For the reentry vehicle, the track performance is to be verified and the impact point is estimated by applying the simulation through ITS-EKF algorithm. To ensure the proposed algorithm's adequacy, by comparing the track performance and impact point distribution by the ITS-EKF with those of ITS-PF combined with ITS and Particle Filter (PF), it is confirmed that the ITS-EKF algorithm can be used an effective real-time On-line impact point prediction.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1016-1026
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• 2017

An adaptive extended Kalman filter based on the maximum likelihood (EKF-ML) is proposed for detecting voltage sag in this paper. Considering that the choice of the process and measurement error covariance matrices affects seriously the performance of the extended Kalman filter (EKF), the EKF-ML method uses the maximum likelihood method to adaptively optimize the error covariance matrices and the initial conditions. This can ensure that the EKF has better accuracy and faster convergence for estimating the voltage amplitude (states). Moreover, without more complexity, the EKF-ML algorithm is almost as simple as the conventional EKF, but it has better anti-disturbance performance and more accuracy in detection of the voltage sag. More importantly, the EKF-ML algorithm is capable of accurately estimating the noise parameters and is robust against various noise levels. Simulation results show that the proposed method performs with a fast dynamic and tracking response, when voltage signals contain harmonics or a pulse and are jointly embedded in an unknown measurement noise.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.115-120
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• 2008

The EKF(Extended Kalman filter) method which is the state estimation algorithm of nonlinear stochastic system depends on the initial error and the estimated states. Therefore, the divergence of the estimated state can be caused if the initial values of the estimated states are not chosen as approximate real state values. In this paper, the demerit of the existing EKF method is improved using the EKF algorithm transformated by STWS(Single Term Walsh Series). This method linearizes each sampling interval of continous-time system through the derivation of an algebraic iterative equation without discretizing continuous system by the characteristic of STWS, the convergence of the estimated states can be improved. The validity of the proposed method is checked through comparison with the existing EKF method in simulation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.4
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• pp.373-382
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• 2016

In this study, an EKF (Extended Kalman Filter) based database reference navigation using both gravity gradient and terrain data was performed to complement the weakness of using only one type of geophysical DB (Database). Furthermore, a new algorithm which combines the EKF and profile matching was developed to improve the stability and accuracy of the positioning. On the basis of simulations, it was found that the overall navigation performance was improved by the combination of geophysical DBs except the two trajectories in which the divergence of TRN (Terrain Referenced Navigation) occurred. To solve the divergence problem, the profile matching algorithm using the terrain data is combined with the EKF. The results show that all trajectories generate the stable performance with positioning error ranges between 14m to 23m although not all trajectories positioning accuracy is improved. The average positioning error from the combined algorithm for all nine trajectories is about 18 m. For further study, a development of a switching geophysical DB or algorithm between the EKF and the profile matching to improve the navigation performance is suggested.

• International Journal of Highway Engineering
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• v.16 no.5
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• pp.59-66
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• 2014

PURPOSES : This study is to predict the Sound Pressure Level(SPL) obtained from the Noble Close ProXimity(NCPX) method by using the Extended Kalman Filter Algorithm employing the taylor series and Linear Regression Analysis based on the least square method. The objective of utilizing EKF Algorithm is to consider stochastically the effect of error because the Regression analysis is not the method for the statical approach. METHODS : For measuring the friction noise between the surface and vehicle's tire, NCPX method was used. With NCPX method, SPL can be obtained using the frequency analysis such as Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) and Constant Percentage Bandwidth(CPB) Analysis. In this research, CPB analysis was only conducted for deriving A-weighted SPL from the sound power level in terms of frequencies. EKF Algorithm and Regression analysis were performed for estimating the SPL regarding the vehicle velocities. RESULTS : The study has shown that the results related to the coefficient of determination and RMSE from EKF Algorithm have been improved by comparing to Regression analysis. CONCLUSIONS : The more the vehicle is fast, the more the SPL must be high. But in the results of EKF Algorithm, SPLs are irregular. The reason of that is the EKF algorithm can be reflected by the error covariance from the measurements.