• Journal of the Computational Structural Engineering Institute of Korea
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• v.32 no.1
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• pp.45-54
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• 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 Power Electronics
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• v.12 no.1
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• pp.40-48
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• 2012

This paper proposes the State-of-charge (SOC) estimator of a LiPB Battery using the Extended Kalman Filter (EKF). EKF can work properly only with an accurate model. Therefore, the high accuracy electrical battery model for EKF state is discussed in this paper, which is focused on high-capacity LiPB batteries. The battery model is extracted from a single cell of LiPB 40Ah, 3.7V. The dynamic behavior of single cell battery is modeled using a bulk capacitance, two series RC networks, and a series resistance. The bulk capacitance voltage represents the Open Circuit Voltage (OCV) of battery and other components represent the transient response of battery voltage. The experimental results show the strong relationship between OCV and SOC without any dependency on the current rates. Therefore, EKF is proposed to work by estimating OCV, and then is converted it to SOC. EKF is tested with the experimental data. To increase the estimation accuracy, EKF is improved with a single dominant varying parameter of bulk capacitance which follows the SOC value. Full region of SOC test is done to verify the effectiveness of EKF algorithm. The test results show the error of estimation can be reduced up to max 5%SOC.

• 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.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.8 no.3
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• pp.107-110
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• 2009

In this paper, a PN code tracking loop with extended Kalman filter (EKF) is proposed for a direct-sequence spread-spectrum. EKF is used to estimate amplitude and delay in a multipath. fading channel. It is shown that tracking error performance is significantly improved by EKF compared with a conventional tracking loop.

• 한국정보통신설비학회:학술대회논문집
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• 2009.08a
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• pp.112-114
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• 2009

In this paper, a PN code tracking loop with extended Kalman filter (EKF) is proposed for a direct-sequence spread-spectrum. EKF is used to estimate amplitude and delay in a multipath fading channel. It is shown that tracking error performance is significantly improved by EKF compared with a conventional tracking loop.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.310-312
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• 2009

The heterodyne laser interferometer has been widely used in precise measurement field. However, the accuracy is limited by the nonlinearity error caused from incomplete laser sources and nonideal optical components. In this paper, we propose the Dual-EKF which estimates states and weights simultaneously to improve the resolution of heterodyne laser interferometer. As a proof, we demonstrate the effectiveness of our proposed method through experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.4B
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• pp.331-337
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• 2002

In this paper we propose extended Kalman filter (EKF)-trilateration method which associates EKF to conventional radiolocation technique, trilateration in order to improve the accuracy of dynamic orbit determination of geostationary satellite for broadcasting of highly accurate Standard Time/Frequency Signal (STFS). We then compare to analyze the time accuracies of three techniques which are differential mode, trilateration locating one of four calibrated earth stations on a neighboring country, and domestic-only baseline EKF-trilateration. Computer simulations have shown that in spite of domestic-only baseline EKF-trilateration of poor GDOP, it is possible to track and locate satellite with an accuracy of a few hundred meter which is the performance 10 times more accurate than trilateration can provide. Finally we can provide standard time service with the time accuracy better than a few ns (frequency stability : 10$\^$-14/ over 7 days) all around Korea peninsula.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.5
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• pp.418-422
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• 2013

This paper has considered the location estimation problem in two dimension space by using a non-linear filter under non-Gaussian noise in underwater acoustic sensor networks(UASNs). Recently, the extended Kalman filter (EKF) is widely used in location estimation. However, the EKF has a lot of problems in the non-linear system under the non-gaussian noise environment like underwater environment. In this paper, we propose the improved Two-Dimension Particle Filter (TDPF) using the re-interpretation distribution techniques based on the maximum likelihood (ML). Through the simulation, we compared and analyzed the proposed TDPF with the EKF under the non-Gaussian underwater sensor networks. Finally, we determined that the TDPF's result shows more accurate localization than EKF's result.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.2
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• pp.81-89
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• 2008

Improved EKF suggests variable path prediction to reduce the event traffic caused by the information sharing among multi-users in networked virtual environment. The three dimensional virtual space is maintained consistently by endless status information exchange among dispersed users, and periodic status transmission brings traffic overhead in network. By using the error between the measured movement trace of dynamic information and the EKF predicted, we propose the method applied to predict the mobile packet of dynamic data which is simultaneously changing. And, the simulation results of DIS dead reckoning algorithms and EKF path prediction is compared here. It followed the specific path and while moving, the proposed method which it proposes predicting with DIS dead reckoning algorithm and to compare to the mobile path of the actual object and it got near it predicts the possibility of knowing it was.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.185-186
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• 2010