• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.9
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• pp.929-936
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• 1991

An estimator for a discrete nonlinear system is derived in the sense of minimum mean square error. An optimal estimator for nonlinear system is very difficult to find and it will be infinite dimensional even if it is found. It has been known that the statistical linearization technique makes it possible to obtain a finite dimensional estimator. In this paper, the procedure of its derivation using the statistical linearization technique that gives an exact mean and variance information is introduced in the sense of minimum mean square error. The derived estimator cannot be clainmed to be globally optimal estimator because it uses the Gaussian assumption to the non-Gaussian distributed nonlinear output. However, the proposed filter exhibits a better performance compared to extended Kalman filter. Simulation results of a simple example present the improvement of the proposed filter in convergent property over the extended Kalman filter.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.134-140
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• 1999

The robust generalized H2 filtering problem for a class of discrete time uncertain linear systems satisfying the sum quadratic constraints(SQCs) is considered. The objective of this paper is to develop robust stability condition using SQCs and design a robust generalized Ha filter to take place of the existing robust Kalman filter. The robust generalized H2 filter is designed based on newly derived robust stability condition. The robust generalized Ha filter bounds the energy to peak gain from the energy bounded exogenous disturbances to the estimation errors under the given positive scalar ${\gamma}$. Unlike the robust Lalman filter, it does not require any spectral assumptions about the exogenous disturbances . Therefore the robust generalized H2 filter can be considered as a deterministic formulation of the robust Kalman filter. Moreover, the variance of the estimation error obtained by the proposed filter is lower than that by the existing robust Kalman filter. The robustness of the robust generalized H2 filter against the uncertainty and the exogenous signal is illustrated by a simple numerical example.

• Journal of Sensor Science and Technology
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• v.25 no.6
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• pp.418-422
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• 2016

A filtering algorithm based on the decentralized moving average Kalman filter with uncertainties is proposed in this paper. The proposed filtering algorithm presented combines the Kalman filter with the moving average strategy. A decentralized fusion algorithm with the weighted sum structure is applied to the local moving average Kalman filters (LMAKFs) of different window lengths. The proposed algorithm has a parallel structure and allows parallel processing of observations. Hence, it is more reliable than the centralized algorithm when some sensors become faulty. Moreover, the choice of the moving average strategy makes the proposed algorithm robust against linear discrete-time dynamic model uncertainties. The derivation of the error cross-covariances between the LMAKFs is the key idea of studied. The application of the proposed decentralized fusion filter to dynamic systems within a multisensor environment demonstrates its high accuracy and computational efficiency.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.433-436
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• 2003

The estimation of the position and the orientation for the mobile robot constitutes an important problem in mobile robot navigation. Although the odometry can be used to describe the motions of the mobile robots, there inherently exist the gaps between the real robots and the mathematical model, which may be caused by a number of error sources contaminating the encoder outputs. Hence, applying the standard extended Kalman filter for the nominal model is not supposed to give the satisfactory performance. As a solution to this problem, a new robust extended Kalman filter is proposed based on the Krein space approach. We consider the uncertain discrete time nonlinear model of the mobile robot that contains the uncertainties represented as sum quadratic constraints. The proposed robust filter has the merit of being constructed by the same recursive structure as the standard extended Kalman filter and can, therefore, be easily designed to effectively account for the uncertainties. The simulations will be given to verify the robustness against the parameter variation as veil as the reliable performance of the proposed robust filter.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.97-107
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• 2002

In this paper, we propose the concept of orthonormalized compressed measurement for the stability analysis of discrete linear time-varying Kalman filters. Unlike previous studies that deal with the homogeneous portion of Kalman filters, the proposed Lyapunov method directly deals with the stochastically-driven system. The orthonorrmalized compressed measurement provides information on the a priori state estimate of the Kalman filter at the k-th step that is propagated from the a posteriori state estimate at the previous block of time. Since the complex multiple-step propagations of a candidate Lyapunov function with process and measurement noises can be simplified to a one-step Lyapunov propagation by the orthonormalized compressed measurement, a stochastic radius of attraction can be derived that would be impractically difficult to obtain by the conventional multiple-step Lyapunov method.

• Water for future
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• v.24 no.3
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• pp.83-94
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• 1991

In this paper a discrete extended Kalman filter for the tidal prediction has been developed. The filter is based on a set of difference equations derived from the one dimensional shallow water equations using the finite difference scheme proposed by Lax-Wendroff. The filter gives estimates of the water level and water velocity, together with the parameters in the model which essentially have a random character, e.g. bottom friction and wind stress. The estimates are propagated and updated by the filter when the physical circumstances change. The Kalman-filter is applied to field data gathered in the coastal area alon the West Sea and it is shown that the filter gives satisfactory results in forecasting the waterlevels during storm surge periods.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.113-120
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• 1999

One of the important challenges facing control engineers in developing automated machineryis to be able to monitor the machines using remote sensors. Observrs are often used to reconstruct the machine variables of interest. However, conventional observers are unalbe to observe the machine variables when the machine models, upon which the observers are based, have inputs that cannot be measured. Since this is often the case, the authors previsously developed a steady-state optimal state and input observer for time-invariant systems [1], this paper extends that work to time-variant systems. A recursive observer, similar to a Kalman-Bucy filter, is developed . This optimal observer minimizes the trace of the error variance for discrete , linear , time-variant, stochastic systems with unknown inputs.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.5
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• pp.299-305
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• 2014

In this paper, a simple version of the hybrid navigation system using Kalman filter is proposed. The implemented hybrid navigation system is composed of a GPS to measure the position and the velocity, and a IMU(inertial measurement unit) to measure the acceleration and the posture of a mobile robot. A discrete Kalman filter is applied to provide the position of the robot by fusing both of the sensor data. When GPS signal is available, the navigation system estimates the position of the robot from the Kalman filter using position and velocity from GPS, and acceleration from IMU. During the interval until next GPS signal arrives, the system calculates the position of the robot using acceleration from IMU and velocity obtained at the previous step. Performance of the navigation system is verified by comparing the real path and the estimated path of the mobile robot. From experiments, we conclude that the navigation system is acceptable for the mobile robot.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.3
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• pp.219-226
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• 1992

In this study, Kalman filtering theory is applied to the estimation of symmetrical components from fault voltage and current signal when it comes to faults with the power system. An algorithm for estimating fault location accurately and quickly by calculating the symmetrical components from the extracted fundamental voltage phasor and current phasor is presented. Also, to confirm the validity of digital distance relaying techniques using Kalman filtering, the experimental results obtained by using the digital simulation of power system is shown.

• Journal of the Korean Society for Railway
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• v.11 no.2
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• pp.203-210
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• 2008

Since hub wheel BLDC Motor consisted of wheel and BLDCM (Brushless DC Motor) without gear reducer has high efficiency and low operation noise, it can be utilized to a driving wheel at some light rail systems. However, installing sensors for speedometer on a Hub-Wheel motor is not easy, so it requires a different speed control mechanism method for speed measurement. This paper introduces a speed control method based on simple mathematical model which uses discrete Kalman Filter to estimate and control the speed of the motor.