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

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.576-579
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• 2008

Electrical impedance(EIT) for the multi-phase flow visualization is an imaging modality in which the resistivity distribution of the unknown object is estimated based on the known sets of injected currents and measured voltages on the surface of the object. In this paper, an EIT reconstruction algorithm based on the extended Kalman filter(EKF) is proposed. The EIT reconstruction problem is formulated as a dynamic model which is composed of the state equation and the observation equation, and the unknown resistivity distribution is estimated recursively with the aid of the EKF. To verify the reconstruction performance of the proposed algorithm, experiments with simulated multi-phase flow are performed.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.2
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• pp.218-224
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• 2009

This paper proposes an effective SLAM based on redundant extended Kalman filter for robot navigation in an irregular surface to enhance the accuracy of robot's pose. To establish an accurate model of a caterpillar type robot is very difficult due to the mechanical complexity of the system which results in highly nonlinear behavior. In addition, for robot navigation on an irregular surface, its control suffers from the uncertain pose of the robot heading closely related to the condition of the floor. We show how this problem can be overcome by the proposed approach based on redundant extended Kalman filter through some computer simulation results.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.158-163
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• 2010

In this paper, we propose the location measurement algorithm of unknown tag based on RFID (Radio-Frequency IDentification) by using RSSI (Received Signal Strength Indication) and TDOA (Time Difference of Arrival) and extended Kalman filter in smart space. To do this, first, we recognize the location of unknown tag by using the RSSI and TDOA recognition methods. Second, we set the coordinate of the tag location measured by using trilateration and SX algorithm. But the tag location data measured by this method are included complex environmental error. So, we use the extended Kalman filter in order to revise error data of the tag location. Finally, we validate the applicability of the proposed method though the simulation in a complex environment.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.2
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• pp.80-86
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• 2010

We propose a spacecraft attitude estimation algorithm using a federated unscented Kalman filter. For nonlinear spacecraft systems, the unscented Kalman filter provides better performance than the extended Kalman filter. Also, the decentralized scheme in the federated configuration makes a robust system because a sensor fault can be easily detected and isolated by the fault detection and isolation algorithm through a sensitivity factor. Using the proposed algorithm, the spacecraft can continuously perform a given mission despite navigation sensor faults. Numerical simulation is performed to verify the performance of the proposed attitude estimation algorithm.

• Journal of KIISE:Software and Applications
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• v.31 no.2
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• pp.245-254
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• 2004

This paper deals with the problem of estimating structure and motion from long continuous image sequences, applying the Expectation Maximization algorithm based on extended Kalman smoother to impose the time-continuity of the motion parameters. By repeatedly estimating the state transition matrix of the dynamic equation and the parameters of noise processes in the dynamic and measurement equations, this optimization gives the maximum likelihood estimates of the motion and structure parameters. Practically, this research is essential for dealing with a long video-rate image sequence with partially unknown system equation and noise. The algorithm is implemented and tested for a real image sequence.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.7
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• pp.898-902
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• 1999

In this paper, we proposed an algorithm for estimating parameters of nonlinear continuous-discrete state-space system. This algorithm uses the conventional extended Kalman filter(EKF) for estimating state variables, and modifies the recursive prediction error method for parameter estimation of the nonlinear system. Simulation results for both linear and nonlinear measurements under the environment of process and measurement noises show a convincing performance of the proposed algorithm.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.671-674
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• 1999

In the PWM converter, PLL(Phase Locked Loop) is usually used as a tool which senses the angle of input voltage. This is sensitive to nois and needs additional hardware. In this work, we propose a sensorless control scheme of PWM converter using EKF(Extended Kalman Filter). EKF estimates a phase angle of input voltage from nonlinear state equation using measured phase currents. We control power factor and DC-link voltage utilizing the estimated phase angle. We demonstrate the effectiveness of the proposed estimation algorithm through simulations.

• Speech Sciences
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• v.11 no.4
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• pp.53-66
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• 2004

The nonlinear speech enhancement method with interactive parallel-extended Kalman filter is applied to speech contaminated by additive white noise. To represent the nonlinear and nonstationary nature of speech. we assume that speech is the output of a nonlinear prediction HMM (NPHMM) combining both neural network and HMM. The NPHMM is a nonlinear autoregressive process whose time-varying parameters are controlled by a hidden Markov chain. The simulation results shows that the proposed method offers better performance gains relative to the previous results [6] with slightly increased complexity.

• Journal of the Society of Naval Architects of Korea
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• v.40 no.5
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• pp.43-52
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• 2003

Two methods which estimate manoeuvring derivatives in the model of hydrodynamic force and moment acting on a manoeuvring ship using sea trial data were compared. One is the widely used parameter estimation method by using the Extended Kalman Filter (EKF), which estimates state variables of linearized state space model at every instant after dealing with the coefficients as the augmented state variables. The other one is the Estimation-Before-Modeling (EBM) technique, so called the two-step method. In the first step, hydrodynamic force of which dynamic model is assumed the third-order Gauss-Markov process is estimated along with motion variables by the EKF and the modified Bryson-Frazier smoother. Then, in the next step, manoeuvring derivatives are identified through the regression analysis. If the exact structure of hydrodynamic force could be known, which was an ideal case, the EKF method would be regarded as being more superior compared to the EBM technique. However the EBM technique was more robust than the EKF method from a realistic point of view where the assumed model structure was slightly different from the real one.