• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.188-188
• /
• 2000

The most important problem in target tracking can be said to be modeling the tracking system correctly. Although the simple linear dynamic equation for this model has used until now, the satisfactory performance could not be obtained owing to uncertainties of the real systems in the case of designing the filters baged on the dynamic equations. In this paper, we propose the extended robust Kalman filter (ERKF) which can be applied to the real target tracking system with the parameter uncertainties. A nonlinear dynamic equation with parameter uncertainties is used to express the uncertain system model mathematically, and a measurement equation is represented by a nonlinear equation to show data from the radar in a Cartesian coordinate frame. To solve the robust nonlinear filtering problem, we derive the extended robust Kalman filter equation using the Krein space approach and sum quadratic constraint. We show the proposed filter has better performance than the existing extended Kalman filter (EKF) via 3-dimensional target tracking example.

• Proceedings of the KIEE Conference
• /
• 1999.07b
• /
• pp.649-652
• /
• 1999

In this paper, the design method of target tracking systems using the extended $H^{\infty}$ filter(EHF) is proposed. Usually, a Cartesian coordinate frame is tell suited to describe the target dynamics. However, the measurements made in radar-centered polar coordinates are expressed as nonlinear equations in Cartesian coordinates. Thus the tacking problem is concerned with the nonlinear estimation. The extended $H^{\infty}$ filter is able to deal with the problems arising in the target tacking systems such as the parameter uncertainty included inevitably in modeling physical systems mathematically, the unavailableness of the stochastic information about exogenous disturbances, and errors due to the linearization of measurement equations. We show the proposed filter is robuster than the extended Kalman filter(EKF) through a simple target tracking example.

• Journal of the Ergonomics Society of Korea
• /
• v.29 no.6
• /
• pp.861-868
• /
• 2010

This study explored an extended three-dimensional Fitts' law that is more suited for the pointing task than the conventional Fitts' law. The experiments were conducted under the manipulation of the distance to the target, size of the target, and direction of the target's location that can be described by two angles, $\theta1$ and $\theta2$. Considering the starting point as the center of coordinates, $\theta1$ is the angle between the positive z-axis and the target location and $\theta2$ is the angle between the positive y-axis and the projected target location on the x-y plane. From the experimental results, we confirmed that all four variables significantly affect the movement time. As we extended the index of difficulty of the conventional Fitts' model by incorporating $\theta1$ and $\theta2$, we established an extended Fitts' model that showed better accordance with the empirical data than the conventional Fitts' model and 3D Fitts' law of Murata and Iwase, in terms of the $r^2$ and the standard error of the residual between the measured movement time and the predicted value.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.12 no.3
• /
• pp.238-244
• /
• 2012

A modeling of vision based robot formation control system using fuzzy logic controller and extended Kalman filter is presented in this paper. The main problems affecting formation controls using fuzzy logic controller and vision based robots are: a robot's position in a formation need to be maintained, how to develop the membership function in order to obtain the optimal fuzzy system control that has the ability to do the formation control and the noise coming from camera process changes the position of references view. In order to handle these problems, we propose a fuzzy logic controller system equipped with a dynamic output membership function that controls the speed of the robot wheels to handle the maintenance position in formation. The output membership function changes over time based on changes in input at time t-1 to t. The noises appearing in image processing change the virtual target point positions are handled by Extended Kalman filter. The virtual target positions are established in order to define the formations. The virtual target point positions can be changed at any time in accordance with the desired formation. These algorithms have been validated through simulation. The simulations confirm that the follower robots reach their target point in a short time and are able to maintain their position in the formation although the noises change the target point positions.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.36 no.12
• /
• pp.908-917
• /
• 1987

Most of moving targets are modelled as nonlinear dynamic equations. In recent years, the extended Kalman filter is frequently used for estimating their behaviors. The conditional Gaussian filter is more suitable than extended kalman filter in the filtering problem of nonlinear systems. But extended Kalman filter and conditional Gaussian filter often do not give optimal estimates and fail to track target trajectories because of its properties. Therefore it is desirable to use adaptive techniques to adapt target maneuvers. In this paper, we will discuss adaptive filtering technique using innovation process based on extended Kalman filter in real time, and suggest another maneuver estimation method using MRAS technique.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.313-314
• /
• 2013

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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.11
• /
• pp.5491-5505
• /
• 2017

Use of the Gaussian inverse Wishart PHD (GIW-PHD) filter has demonstrated promise as an approach to track an unknown number of extended targets. However, the partitioning approaches used in the GIW-PHD filter, such as distance partition with sub-partition (DP-SP), prediction partition (PP) and expectation maximization partition (EMP), fails to provided accurate partition results when targets are spaced closely together and performing maneuvers. In order to improve the performance of a GIW-PHD filter, this paper presents a cooperation partitioning (CP) algorithm to solve the partitioning issue when targets are spaced closely together. In the GIW-PHD filter, the DP-SP is insensitive to target maneuvers but sensitive to the differences in target sizes, while EMP is the opposite. The proposed CP algorithm is a fusion approach of DP-SP and EMP, which employs EMP as a sub-partition approach after DP. Therefore, the CP algorithm will be sensitive to neither target maneuvers nor differences in target sizes. The simulation results show that the use of the proposed CP algorithm will improve the performance of the GIW-PHD filter when targets are spaced closely together.

• Journal of the Korean Society of Safety
• /
• v.32 no.6
• /
• pp.29-33
• /
• 2017

In order to solve this sort of electrostatic failure in Display and Semiconductor process, Soft X-ray ionizer is mainly used. Soft X-ray Ionizer does not only generate electrical noise and minute particle but also is efficient to remove electrostatic as it has a wide range of ionization. There exist variable factors such as type of tungsten thickness deposited on target, Anode voltage etc., and it takes a lot of time and financial resource to find optimal performance by manufacturing with actual X-ray tube source. Here, MCNPX (Monte Carlo N-Particle Extended) is used for simulation to solve this kind of problem, and optimum efficiency of X-ray generation is anticipated. In this study, X-ray generation efficiency was compared according to target material thickness using MCNPX and actual X-ray tube source under the conditions that tube voltage is 5 keV, 10 keV, 15 keV and the target Material is Tungsten(W). At the result, In Tube voltage 5 keV and distance 100 mm, optimal target thickness is $0.05{\mu}m$ and fastest decay time appears + decay time 0.28 sec. - deacy time 0.30 sec. In Tube voltage 10keV and distance 100 mm, optimal target Thickness is $0.16{\mu}m$ and fastest decay time appears + decay time 0.13 sec. - deacy time 0.12 sec. In the tube voltage 15 keV and distance 100 mm, optimal target Thickness is $0.28{\mu}m$ and fastest decay time appears + decay time 0.04 sec. - deacy time 0.05 sec.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.657-659
• /
• 1998

In this paper we discuss an algorithm to discriminate a target under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of both the sequences of processed waveform signature and the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. Numerical experiments on various scenarios show that the proposed algorithm discriminates the target faster with a higher probability of success than the algorithm using only the innovation sequences from extended Kalman filters.