• The Journal of the Acoustical Society of Korea
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• 제20권1E호
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• pp.25-30
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• 2001

The unstable signature that is defined as frequency change with respect to the time or frequency modulation, is caused by the external loading variation in specific machinery component and Doppler shift etc. In this study, we analyze the generation mechanism of the unstable signature and apply the Extended Kalman filter (EKF) algorithm for its detection. The performance of Extended Kalman Filter is examined for numerical and measured signals and the results show its validity for unstable signature detection.

• 한국통신학회논문지
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• 제37A권11호
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• pp.1001-1011
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• 2012

본 논문에서는 IEEE 802.11 시스템에서 경쟁 중인 터미널 수를 추정하고 이를 반영할 때 시스템 성능에 미치는 영향을 분석한다. IEEE 802.11 시스템에서는 터미널간의 다중 접근의 방법으로 DCF (Distributed Coordination Function)를 이용하고 있으며 경쟁하는 터미널 수를 정확하게 추정하여 반영하는 것이 시스템 throughput 증가하는데 중요한 요소가 된다. 본 논문에서는 터미널 수를 추정하는 방법으로 노이즈 정보가 필요하지 않는 Extended H Infinity Filter (EHIF)를 이용하여 터미널 수를 추정하는 방법을 제안한다. 경쟁하는 터미널의 수가 saturated되는 경우와 non-saturated되는 네트워크 환경에서 EHIF가 기존의 Extended Kalman Filter (EKF) 방법보다 좋은 성능을 가짐을 모의실험을 통해 확인하였고 이를 정량적으로 분석하였다.

• 한국기계가공학회지
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• 제15권2호
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• pp.131-138
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• 2016

In order to improve the performance of an extended Kalman filter, a simplified indirect inference method (SIIM) fuzzy logic system (FLS) is proposed. The proposed FLS is composed of two fuzzy input variables, four fuzzy rules and one fuzzy output. Two normalized fuzzy input variables are the variance between the trace of a prior and a posterior covariance matrix, and the residual error of a Kalman algorithm. One fuzzy output variable is the weighting factor to adjust for the Kalman gain. There is no need to decide the number and the membership function of input variables, because we employ the normalized monotone increasing/decreasing function. The single parameter to be determined is the magnitude of a universe of discourse in the output variable. The structure of the proposed FLS is simple and easy to apply to various nonlinear state estimation problems. The simulation results show that the proposed FLS has strong adaptability to estimate the states of the incoming/outgoing moving objects, and outperforms the conventional extended Kalman filter algorithm by providing solutions that are more accurate.

• 한국공간구조학회논문집
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• 제2권1호
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• pp.41-49
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• 2002

In this paper, a study of 2-step damage detection for space truss structures using the extended Kalman filter theory is presented. Space truss structures are composed of many members, so it is difficult to find damaged member from the whole system. Therefore, 2-step damage identification method is applied to detect the damaged members. First, kinetic energy change ratio is used to find damage region including damaged member and then detect damaged member using extended Kalman filtering algorithm in damage region. The effectiveness of proposed method is verified through the numerical examples.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1999년도 전력전자학술대회 논문집
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• pp.544-548
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• 1999

Using Observer, on the sensorless vector control system is a novel techniques for modern induction motor control. In this paper, a speed estimation algorithm of an induction motor using an extended kalman filter was proposed. Extended kalman filter can solve the problem, that have steady state error of estimated speed in flux and slip estimation method. The extended Kalman filter is employed to identify the speed of an induction motor and rotor flux based on the measured quantities such as stator current and DC link voltage. In order to confirming above proposal, computer simulation carried out using Matlab Simulink and show the effectiveness of the control drives for induction motor speed estimation.

• 대한전기학회논문지
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• 제38권12호
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• pp.971-982
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• 1989

An integrating position estimation algorithm has been developed for the navigation system of a free-ranging AGV system. The navigation system focused in this research work consists of redundant wheel encoders for the relative position measurement and a vision sensor for the absolute position measurement. A maximum likelihood method and an extended Kalman filter are implemented for enhancing the performance of the position estimator. The maximum likelihood estimator processes noisy, redundant wheel encoder measurements and yields efficient estimates for the AGV motion between each sampling interval. The extended Kalman filter fuses inharmonious positional data from the deadreckoner and the vision sensor and computes the optimal position estimate. The simulation results show that the proposed position estimator solves a generalized estimation problem for locating the vehicle accurately in space.

• 한국소음진동공학회논문집
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• 제19권3호
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• pp.294-300
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• 2009

In high speed railway bridges, dynamic analysis is important because of high passing velocity and moving load at the regular intervals, and damping ratio is a major parameter to predict dynamic responses. In this paper, damping ratios were estimated by using half power band width method and extended Kalman filter according to acceleration signal conditions, and a relationship between estimated damping ratios and representative values of bridge vibration was derived. From the results, damping ratios estimated from total ambient vibration were more reliable than only free vibration part. In case of using extended Kalman filter, the estimated damping ratios varying with RMQ(root mean quad), as one of representative values of bridge vibration, have more feasible trend. Thus, it is shown that further studies on reliabilities of estimated damping ratios are needed.

• Transactions on Electrical and Electronic Materials
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• 제17권1호
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• pp.13-17
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• 2016

The ampere-hour integral method and the open circuit voltage method are integrated via the extended Kalman filter method so as to overcome insufficiencies of the ampere-hour integral method and the open circuit voltage method for estimating battery SOC. The process noise covariance and the measurement noise covariance of the extended Kalman filter method are simplified based on the Thevenin equivalent circuit model, with a proposed simplified SOC estimating method. Verification of DST experiments indicated that the battery SOC estimating method is simple and feasible, and the estimated SOC error is no larger than 2%.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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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.

• 제어로봇시스템학회논문지
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• 제16권7호
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.