• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.472-475
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• 2005

In this paper, we propose a Kalman-fuzzy control method using multi-sensor. The proposed method is to obtain a robust sensor data and to integrate multi-sensor data using fuzzy system. in order to verify the effectiveness of the proposed method, we performed the simulation for the position of an elertro hydraulic actuator. The results showed a good performance.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2615-2617
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• 2003

The Kalman filter is a recursive Linear Estimator for the linear dynamic systems(LDS) affected by two different noises called process noise and measurement noise both of which are uncorrelated white. The Expectation Maximization(EM) algorithm is employed in this paper as a preprocessor to reinforce the effectiveness of Kalman estimator. Particularly, we focus on the relation between Kalman filter and EM algorithm in the LDS. In this paper, we propose a new algorithm to improve the performance on the parameter estimation via EM algorithm, which improves the overall process of Kalman filtering. Since Kalman filter algorithm not only needs the system parameters but also is very sensitive the initial state conditions, the initial conditions decided through EM turns out to be very effective. In experiments, the computer simulation results ate provided to demonstrate the superiority of the proposed algorithm.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.331-340
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• 2000

This study aims to develop a methodology of real time forecasting of mundation risk based on DAMBRK model and Kalman filter. The model is based on implicit, nonlinear finite difference approximatIons of the one-dimensional dynamic wave equations. The stochastic estimator uses on extended Kalman filter to provide optimal updating estimates. These are accomplished by combining the predictions of the determurustic model with real time observauons modified by the Kalman filter gain ractor. Inundation risks are also estimated by applying Monte Carlo simulation to consider the variability in cross section geometry and Manning's roughness coefficient. The model calibrated by applying to the floods ot South Han River on September, 1990 and August, 1995. The Kalman tilter model indicates that significant improvement compared to deteriministic analysis in flood routing predictions in the river. Overtopping risk of levee is also presented by comparing levee height with simulated flood level. level.

• Journal of Biomedical Engineering Research
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• v.27 no.3
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• pp.131-141
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• 2006

A visual decision by clinical experts like physical therapists is a best way to detect onset and offset time of muscle activation. The current computer-based algorithms are being researched toward similar results of clinical experts. The new algorithm in this paper has an ability to extract a trend from noisy input data. Kalman smoother is used to recognize the trend to be revealed from disorderly signals. Histogram of smoothed signals by Kalman smoother has a clear boundary to separate muscle contractions from relaxations. To verify that the Kalman smoother algorithm is reliable way to detect onset and offset time of muscle contractions, the algorithm of Robert P. Di Fabio (published in 1987) is compared with Kalman smoother. For 31 templates of subjects, an average and a standard deviation are compared. The average of errors between Di Fabio's algorithm and experts is 109 milliseconds in onset detection and 142 milliseconds in offset detection. But the average between Kalman smoother and experts is 90 and 137 milliseconds in each case. Moreover, the standard deviations of errors are 133 (onset) and 210 (offset) milliseconds in Di Fabio's one, but 48 (onset) and 55 (offset) milliseconds in Kalman smoother. As a result, the Kalman smoother is much closer to determinations of clinical experts and more reliable than Di Fabio's one.

• Journal of the Korean Institute of Navigation
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• v.7 no.2
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• pp.1-45
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• 1983

This paper concerns the applications of the Kalman filter to navigation and the develment of computer programs of the navigational calculations. Methods to apply the Kalman filter to celestial fix, fix by cross bearing and cocked hat are proposed, and numerical simulations under various noise conditiions are conducted. The accuracy of the optimal positions obtained by the Kalman filter is compared with that of the fixed positiions by radial error method. In the case of celestial fix, an algorithm to estimate the optimal positions by using the linear Kalman filter is presented. The optimal positions by the Kalman filter are compared with the running fixes and with the most probable positions obtained from a single line of position. It is confirmed that the resutls of the proposed method are more accurate than the others. In practical piloting, bearings are generally measured intermittently and the measurement process is nonlinear. It is, therefore, difficult for us to apply the Kalman filter to fix by cross bearing. In order to be used in such an unfavorable case, the extended Kalman filter is revised and the aplicability of the revised extended Kalman filter is checked by numerical simulation under various noise conditions. In a cocked hat, an inside or outside fix is dependent only upon azimuth spread, if the error of each line of position is assumed to be equal both in magnitude and sign. A new technique of selecting a ship's position between an inside fix and an outside fix in a cocked hat by using fix determinant derived from the equation of three lines of position is also presented. The relations among the optimal position by Kalman filter, incentre (or excentre) and random error centtre of the cocked hat are discussed theoretically and the accuracy of the optimal position is compared with that of the others by numerical simulation.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1589-1597
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• 2021

Due to the low output rate and time delay of vehicle's navigation results, the electro-optical tracking system(EOTS) cannot estimate accurate target positions. If an inertial measurement unit(IMU) is additionally mounted into the EOTS and inertial navigation system(INS) is constructed, the high navigation output rate can be obtained. And the time-delay can be compensated by using the augmented Kalman filter. An accurate initial attitude is required in order to have accurate navigation outputs. In this paper, an attitude determination algorithm is proposed using the augmented Kalman filter in order to compensate measurement delay of the EOTS and have accurate initial attitude. The proposed initial attitude determination algorithm consists of an augmented Kalman filter, an INS, and an integrated Kalman filter. The augmented Kalman filter compensates the time-delay of the vehicle's navigation results and the integrated Kalman filter estimates the navigation error of the INS. In order to evaluate performance of the proposed algorithm, vehicle's navigation outputs and IMU measurements were generated using sensors' model-based measurement generator and initial attitude estimation errors of the proposed algorithm and the conventional algorithm without the augmented Kalman filter were compared for the generated measurements. The evaluation results show that the proposed algorithm has better accuracy.

• Journal of Internet Computing and Services
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• v.7 no.3
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• pp.71-82
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• 2006

This paper propose a realtime tracking algorithm of mobile object in indoor environment. this proposed system selects location data closer to mobile objects in real time that are results of Triangulation method and DCM(Database Correlation Method)method. Also, this system applies adjusted location data selected by using Kalman filter, and in result it improved location accuracy of transfer object. Be studied in existing the Kalman filter have unstable location data until its settlement because of it extracts current values by using the past the information. However, proposed location tracking system don't apply existent Kalman filter to this system and it permits precisional tracking location by uses more effective methods.

• Nuclear Engineering and Technology
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• v.26 no.2
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• pp.205-211
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• 1994

The best method is selected among the 3 digital dynamic compensation methods which are developed or applied for the Rhodium self-powered neutron detector. The three digital dynamic compensation methods are the existing Dominant Pol Tustin method of the COLSS(Core Operating Limit Supervisory System), the Direct Inversion method and Kalman Filter method. The Direct Inversion method is an improved method of D. Hoppe and R. Maletti and the Kalman Filter method is developed using the Kalman Filter. Response times of the compensated signals to achieve 90% of a step input are 28.1, 17.2 and 6.5 seconds respectively for the same noise gain telling that the Kalman Filter method is the best amens the 3 methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.5
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• pp.552-559
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• 2005

Recurrent neural networks(RNNs) trained with gradient based such as real time recurrent learning(RTRL) has a drawback of slor convergence rate. This algorithm also needs the derivative calculation which is not trivialized in error back propagation process. In this paper a derivative free Kalman filter, so called the unscented Kalman filter(UKF), for training a fully connected RNN is presented in a state space formulation of the system. A derivative free Kalman filler learning algorithm makes the RNN have fast convergence speed and good tracking performance without the derivative computation. Through experiments of nonlinear channel equalization, performance of the RNNs with a derivative free Kalman filter teaming algorithm is evaluated.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.593-607
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• 2022

To solve the problem of detecting structural damage, a two-stage method using the Kalman filter and Particle Swarm Optimization (PSO) is proposed. In this method, the first PSO population is enhanced using the Kalman filter method based on dynamic responses. Due to noise in the sensor responses and errors in the damage detection process, the accuracy of the damage detection process is reduced. This method proposes a novel approach for solve this problem by integrating the Kalman filter and sensitivity analysis. In the Kalman filter, an approximate damage equation is considered as the equation of state and the damage detection equation based on sensitivity analysis is considered as the observation equation. The first population of PSO are the random damage scenarios. These damage scenarios are estimated using a step of the Kalman filter. The results of this stage are then used to detect the exact location of the damage and its severity with the PSO algorithm. The efficiency of the proposed method is investigated using three numerical examples: a 31-element planer truss, a 52-element space dome, and a 56-element space truss. In these examples, damage is detected for several scenarios in two states: using the no noise responses and using the noisy responses. The results show that the precision and efficiency of the proposed method are appropriate in structural damage detection.