• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1056-1061
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• 2009

This paper proposes a short-term water demand forecasting algorithm based on kalman filtering with data mining for sustainable water supply and effective energy saving. The proposed algorithm utilizes a mining method of water supply data and a decision tree method with special days like Chuseok. And the parameters of MLAR (Multi Linear Auto Regression) model are estimated by Kalman filtering algorithm. Thus, we can achieve the practicality of the proposed forecasting algorithm through the good results applied to actual operation data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.16-21
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• 2005

This paper considers the problem of state estimation in linear continuous-time systems with multi-sensor environment and observation uncertainties. We propose two suboptimal filtering algorithms for these types of systems. The filtering algorithms consist of two steps: The local optimal Kalman estimates are computed at the first step. And, these local estimates are lineally fused at the second step. The implementation of the two-step filtering algorithms needs a lower memory demand than the optimal Kalman and adaptive Lainiotis-Kalman filters. In consequence of parallel structure of the proposed filters, the parallel computers can be used for their design. The examples exhibit the effect of common noise on the performance of fusion of the local Kalman estimates based on observations from different sensors and in the presence of uncertainties.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_1
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• pp.249-256
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• 2024

Traditional methods for monitoring targets rely heavily on probabilistic data association (PDA) or Kalman filtering. However, achieving optimal performance in a densely congested tracking environment proves challenging due to factors such as the complexities of measurement, mathematical simplification, and combined target detection for the tracking association problem. This article analyzes a target tracking problem through the lens of fuzzy logic theory, identifies the fuzzy rules that a fuzzy tracker employs, and designs the tracker utilizing fuzzy rules and Kalman filtering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.12C
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• pp.1208-1215
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• 2009

In OFDM systems, mobile channel degrades the system performance seriously. Therefore, channel estimation technique is required to compensate for the degradation from the channel effects. However, conventional channel estimations in frequency domain induce ICI which is induced from Doppler frequency. In addition, a linear interpolation method causes inaccurate channel estimation. In order to minimize the effect of the interference and interpolation error, the proposed method combines LS method and Kalman filtering algorithm. Channel impulse response is adaptively tracked by Kalman filtering based on the information from LS estimator. Simulation results are presented to verify the performance of the proposed channel estimation over mobile channel environment. Simulation results show that the proposed method can effectively compensate for channel degradation.

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.3
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• pp.1-11
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• 1980

For an ordinary engineer or researcher there are too diversified branches or even disciplines which have their own jargon to complicate an easy access, Nevertheless in many cases an isomorphism or even identity of notions exist to escape our grasp when expressed in different discipline or context, In this paper the masterwork of Box and Jenkins is introduced to accustom a few terms of statisticiens, to be followed by the technique of smoothing filtering of Wiener and Kalman - Bucy. The advantages of a transform (for example Hadamard) technique are explaned as well as authors personal philosophical views.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.36 no.12
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• pp.908-917
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• 1987

Most of moving targets are modelled as nonlinear dynamic equations. In recent years, the extended Kalman filter is frequently used for estimating their behaviors. The conditional Gaussian filter is more suitable than extended kalman filter in the filtering problem of nonlinear systems. But extended Kalman filter and conditional Gaussian filter often do not give optimal estimates and fail to track target trajectories because of its properties. Therefore it is desirable to use adaptive techniques to adapt target maneuvers. In this paper, we will discuss adaptive filtering technique using innovation process based on extended Kalman filter in real time, and suggest another maneuver estimation method using MRAS technique.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.241-245
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• 2005

Strapdown inertial navigation system (SINS) integrated with astronavigation system (ANS) yields reliable mission capability and enhanced navigational accuracy for spacecrafts. The theory and characteristics of integrated system based on unscented Kalman filtering is investigated in this paper. This Kalman filter structure uses unscented transform to approximate the result of applying a specified nonlinear transformation to a given mean and covariance estimate. The filter implementation subsumed here is in a direct feedback mode. Axes misalignment angles of the SINS are observation to the filter. A simple approach for simulation of axes misalignment using stars observation is presented. The SINS error model required for the filtering algorithm is derived in space-stabilized mechanization. Simulation results of the integrated navigation system using a medium accuracy SINS demonstrates the validity of this method on improving the navigation system accuracy with the estimation and compensation for gyros drift, and the position and velocity errors that occur due to the axes misalignments.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.5
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• pp.452-459
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• 1992

In this paper, we have estimated voice traffics using Kalman-filtering for upcoming years and evaluated performance of a grid topology circuit switched network using above outcomes. Since measurement errors and modeling errors are considered in Kalman-filtering, the system is estimated more accurately than by using any other estimation methods. A grid topology circuit switched network has alternative routing paths, therefore it is more reliable than any other networks. In this paper, we have calculated the call blocking probabilities, which is the key measuremens of performance evaluation in circuit switched networks. by using estimated voice traffics for upcoming years.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.311-312
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• 2013