• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.32 no.1
• /
• pp.45-54
• /
• 2019

The damage detection method using the extended Kalman filter(EKF) technique has been continuously used since EKF can estimation the responses of the damaged building structure and the stiffness of the structure. However, in the use of EKF, the requirement of setting the initial paramters P, Q, and R has caused the divergence and instability of the state vector, and various researches have been conducted to determine theses parameters. In this paper, adaptive extended Kalman filter(AEKF) method is proposed to solve the problem of setting the values of P, Q, and R, which are important parameters determining the convergence performance of the EKF state vector. By using the AEKF method proposed in this study, the P, Q, and R parameters are updated every k steps. The proposed algorithm is applied for the estimation of stiffness and the damage detection of 3-DOF problem. Based of the verification, it can be found that the selection process for the values of P, Q, and R can improve the convergence performance of EKF.

• Journal of Astronomy and Space Sciences
• /
• v.25 no.3
• /
• pp.283-290
• /
• 2008

Satellite formation flying can provide the platform for interferometric observation to acquire the precise data and ensure the flexibility for space mission. This paper presents development and verification of an algorithm to estimate the baseline between formation flying satellites. To estimate a baseline(relative navigation) in real time, EKF(Extended Kalman Filter) and UKF(Unscented Kalman Filter) are used. Measurements for updating a state-vector in Kalman Filter are GPS single difference data. In results, The position errors in estimated baseline are converged to less than ${\pm}1m$ in both EKF and UKF. And as using the two types of Kalman filter, it is clear that the unscented Kalman filter shows a relatively better performance than the extended Kalman filter by comparing an efficiency to the model which has a non-linearity.

• International Journal of Control, Automation, and Systems
• /
• v.3 no.2
• /
• pp.173-182
• /
• 2005

In this paper, a target tracking algorithm for tracking maneuvering vehicles is presented. The overall algorithm belongs to the category of an interacting multiple-model (IMM) algorithm used to detect multiple targets using fused information from multiple sensors. First, two kinematic models are derived: a constant velocity model for linear motions, and a constant-speed turn model for curvilinear motions. Fpr the constant-speed turn model, a nonlinear information filter is used in place of the extended Kalman filter. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. The model-matched filter used in multi-sensor environments takes the form of a federated nonlinear information filter. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. In this paper, the structural features and information sharing principle of the federated information filter are discussed. The performance of the suggested algorithm using a Monte Carlo simulation under the two patterns is evaluated.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2498-2501
• /
• 2003

In this paper, we present a wavelet neural network (WNN) approach to the solution of the path tracking problem for mobile robots that possess complexity, nonlinearity and noise. First, we discuss a WNN based control system where the control signals are directly obtained by minimizing the difference between the reference track and the pose of a mobile robot. This compact network structure is helpful to determine the number of hidden nodes and the initial value of weights. Then, the data with various noises provided by odometric and external sensors are here fused together by means of an Extended Kalman Filter (EKF) approach for the pose estimation problem of mobile robots. This control process is a dynamic on-line process that uses the wavelet neural network trained via the gradient-descent method with estimates from EKF. Finally, we verify the effectiveness and feasibility of the proposed control system through simulations.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2000.04b
• /
• pp.207-214
• /
• 2000

In this paper, a study of the effenctiveness and convergency of five damage measures for structural damage detection of 2-dimensional truss structure using the extended Kalman filtering algorithm is presented. These damage measures are associated with the change in mode shape and displacement due to structural damage. Damage measures contain the change in natural frequency, mode shape, curvature of mode shape, displacement of static force and curvature of displacement of static force. The effectiveness and convergency of these damage measures by using extended Kalman filtering algorithm are demonstrated with the numerical examples.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2008.11a
• /
• pp.82-83
• /
• 2008

In the cases of high speed railway bridge, dynamic behavior analysis is important because of high passing velocity and moving load at the regular intervals, and the damping ratio is a major element in dynamic behavior analysis. In this paper, damping ratios were estimated by two methods and vibration type sections, and relationship between estimated damping ratio and representative value of bridge vibration. At the results, estimated damping ratio using all time of vibration were more stable then using only free vibration section. And in the case of using extended Kalman filter, estimated damping ratio were trend of growth by growth of representative value of bridge vibration. At last, it was shown that study about reliabilities of estimated damping ratios were need.

• The Journal of Korea Robotics Society
• /
• v.5 no.4
• /
• pp.367-376
• /
• 2010

This Paper Proposes A Robust Image Stabilization System For A Mobile Robot Using An Extended Kalman Filter (Ekf). Though Image Information Is One Of The Most Efficient Data Used For Robot Navigation, It Is Subjected To Noise Which Is The Result Of Internal Vibration As Well As External Factors Such As Uneven Terrain, Stairs, Or Marshy Surfaces. The Camera Vibration Deteriorates The Image Resolution By Destroying The Image Sharpness, Which Seriously Prevents Mobile Robots From Recognizing Their Environment For Navigation. In This Paper, An Inclinometer Was Used To Measure The Vibration Angle Of The Camera System Mounted On The Robot To Obtain A Reliable Image By Compensating For The Angle Of The Camera Vibration. In Addition The Angle Prediction Obtained By Using The Ekf Enhances The Image Response Analysis For Real Time Performance. The Experimental Results Show The Effectiveness Of The Proposed System Used To Compensate For The Blurring Of The Images.

• Journal of Astronomy and Space Sciences
• /
• v.12 no.1
• /
• pp.143-156
• /
• 1995

Using a directional antenna, the Doppler effect of satellites can be detected and the orbital elements can be obtained by the Extended Kalman Filter with the observed frequency shift data. We obtained the orbital elements of NOAA-11 by the application of the Extended Kalman Filter type algorithm to the Doppler shift data of NOAA-11d and discussed the accuracy and the credibility of this algorithm.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.25 no.3
• /
• pp.15-23
• /
• 2011

This paper proposes a nonlinear speed controller for a permanent magnet synchronous motor (PMSM). In this paper, the load torque is estimated by an extended Kalman filter (EKF) observer because the proposed controller needs its knowledge. To confirm the effectiveness of the proposed control scheme, simulations and experiments are performed under motor parameter variations with a prototype PMSM drive system.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.05a
• /
• pp.1473-1476
• /
• 2007

유비쿼터스 컴퓨팅과 유비쿼터스 네트워크가 다양하게 발전되고 활용됨에 따라 유비쿼터스 환경에서 LBS(위치 기반서비스) 관련 기술의 중용성도 높아지고 있다. 이런 LBS를 위한 기존의 대표적인 옥내 측위 시스템들을 알아보고, IEEE802.11b 규격의 2.4GHz RF의 RSSI와 거리와의 관계를 기반으로 TDOA를 이용한 방법과 Extended Kalman Filter를 이용한 방법의 측위 시스템을 구현하고 실험을 통한 실측 오차를 비교 분석한다.