• Smart Structures and Systems
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• v.25 no.1
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• pp.37-45
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• 2020

This paper aims at enlightening the properties, computational and implementation issues related to Kalman filter based state estimation algorithms and sliding mode observers, by applying them for estimating the states of a smart structure system. The Kalman based estimators considered in this work are Kalman filter and information filter and, the sliding mode observers considered are Utkin observer and higher order sliding mode observer. A fourth order linear time invariant model of a piezo actuated beam is used in this work. This structure is embedded with four number of piezo patches, of which two act as sensors, one as disturbance actuator and the other as control actuator. The performance of the state estimation algorithms is evaluated through simulation, for the first two vibrating modes of the piezo actuated structure, when the structure is maintained at first mode and second mode resonance.

• Journal of the Korean Society of Industry Convergence
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• v.22 no.5
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• pp.575-582
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• 2019

Advanced driving assist system can support safety of driver and passengers which may require vehicle dynamics states as well as road geometry. It is essential to have in real-time estimation of related variables and parameters. Among the road geometry parameters, road slope angle which can not be measured is essential parameter in pose estimation, adaptive cruise control and others on sag road. In this paper, Kalman filter based method for the estimation of the vehicle dynamics and road slope angle using a nonlinear vehicle model is proposed. It uses a combination of Kalman filter as Cascade Extended Kalman Filter. CEKF uses measured vehicle states such as yaw rate, longitudinal/lateral acceleration and velocity. Unknown vehicle parameters such as center of gravity and inertia are obtained by 2 D.O.F lateral model and experimentally. Simulation and Experimental tests conducted with commercialized vehicle dynamics model and real-car.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.449-451
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• 2003

The estimation of time-series data is fundamental process in many data analysis cases. However, the unwanted measurement error is usually added to true data, so that the exact estimation depends on efficient method to eliminate the error components. The wavelet transform method nowadays is expected to improve the accuracy of estimation, because it is able to decompose and analyze the data in various resolutions. Therefore, the wavelet based Kalman filter method for the estimation of time-series data is proposed in this paper. The wavelet transform separates the data in accordance with frequency bandwidth, and the detail wavelet coefficient reflects the stochastic process of error components. This property makes it possible to obtain the covariance of measurement error. We attempt the estimation of true data through recursive Kalman filtering algorithm with the obtained covariance value. The procedure is verified with the fundamental example of Brownian walk process.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.353-356
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• 2011

본 연구에서는 G/R 비의 실시간 결정을 목적으로 Dual Kalman Filter를 이용하였다. Dual Kalman Filter 는 이중추정(dual estimation)을 기반으로 하는 자료동화기법으로 기존 Kalman Filter와 상이한 상태-공간 모형으로 구성된다. 이에 Dual Kalman Filter와 기존 Kalman Filter의 적용성능을 비교 검토하였으며, 다양한 비교를 위하여 강우의 임계치와 누적시간의 고려여부에 따른 결과를 추가적으로 검토하였다. 두 기법의 적용성능 비교결과 Dual Kalman Filter가 우수한 것으로 나타났다. 이는 Dual Kalman Filter 기법이 G/R 비의 큰 변동성과 이상치를 효과적으로 필터링하고, 시계열 모형의 매개변수를 실시간으로 갱신하여 정확한 예측치를 추정하였기 때문인 것으로 판단된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.4
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• pp.781-790
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• 2008

In order to improve on the divergence by noise convariance in the Kalman filter position estimation, we propose a method of position estimating through compensating the autonomous mobile robot's noise. Proposed method is the modified Kalman filter using neural network. It is prevented the divergence by the estimation of measurement noise covariance and system noise covariance. In order to verify the effectiveness of the proposed method, we performed simulations and experiments for position estimation. The results show that convergence and position error is reduced than the Kalman filter method.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.955-958
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• 1998

This paper describes the algorithm which extracts moving vehicles from sequential images and tracks those vehicles using Kalman filter. This work is composed of a motion segmentation stage which extracts moving objects from sequential images and gets features of objects, and a motion estimation stage which estimates the position and the motion of moving objects using Kalman filter. In the motion estimation stage, applying to affine motion model we divided the Kalman filter into position filter and velocity filter to employ linear Kalman filter. Multi-target tracking requires a data association component that decides which measurement to use for updating the state of which object. We use pattern recognition method to solve this problem.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2005.11a
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• pp.45-46
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• 2005

In this paper, a passive telemetry sensor system using Unscented Kalman Filter(UKF) is proposed. Specially, to show the effective tracking performance of the UKF, we compared with the tracking performance of Recursive Least Square Estimation (RLSE) using linearization.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.2
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• pp.120-126
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• 1997

In this study, an effective estimation method of wheel acceleration is presented. The wheel acceleration is mainly used in the ABS(anti-lick brake system) and the TCS(traction control system). The wheel acceleration is a derivative term of the wheel speed which is generally measured by the wheel speed sensors. The results of a simple differentiation of the signal and an observation of the signal by Kalman filter show that Kalman filter has better performance than the simple differentiation. The differentiated sine signal which is contaminated with random noise shows a rugged signal compared with the signal which is filtered by the Kalman filter. The covariance of the differentiated signal is higher than that of the Kalman-filtered signal, too. The presented Kalman filter technique shows an effective way of solution to get the estimated wheel acceleration value which is sufficient to be applied to ABS or TCS control algorithms.

• Proceedings of the KIPE Conference
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• 1999.07a
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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.

• Journal of Aerospace System Engineering
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• v.10 no.1
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• pp.21-28
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• 2016

Orbit determination problems have been interest of many researchers for long time. Due to the high nonlinearity of the equation of motion and the measurement model, it is necessary to linearize the both equations. To avoid linearization, the filter based on Fokker-Planck equation is designed. with the extended Kalman filter update mechanism, in which the associated Fokker-Planck equation was solved efficiently and accurately via discrete quadrature and the measurement update was done through the extended Kalman filter update mechanism. This filter based on the DQMOM and the EKF update is applied to the orbit determination problem with appropriate modification to mitigate the filter smugness. Unlike the extended Kalman filter, the hybrid filter based on the DQMOM and the EKF update does not require the burdensome evaluation of the Jacobian matrix and Gaussian assumption for the system, and can still provide more accurate estimations of the state than those of the extended Kalman filter especially when measurements are sparse. Simulation results indicate that the advantages of the hybrid filter based on the DQMOM and the EKF update make it a promising alternative to the extended Kalman filter for orbit estimation problems.