• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.145-145
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• 2000

In this paper, we propose a hybrid d-step predictor which is composed of an adaptive predictor and a Kalman predictor. We prove the performance limit of the proposed predictor. Simulation is conducted to examine the performance of the proposed predictor. Simulation results show that the proposed combined predictor is superior to the adaptive predictor and the Kalman predictor. Proposed predictor is used for prediction of gun tip vibration of k1 tank. The result is compared with that of conventional adaptive predictor.

• 한국군사과학기술학회지
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• 제9권3호
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• pp.132-140
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• 2006

In this paper, a new fault detection method for the extended Kalman filter, which uses a N-step predictor, is proposed. The N-step predictor performs the only time propagations for N-step intervals without measurement updates and its output is used as a monitoring signal for the fault detection. A consistency between the extended Kalman filter and the N-step predictor is tested to detect a fault. A test statistic is defined by the difference between the extended Kalman filter and the N-step predictor. The proposed method is applied to strapdown inertial navigation system (SDINS). By computer simulation, it is shown that the proposed method detects a fault effectively.

• 대한전기학회논문지
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• 제39권2호
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• pp.210-217
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• 1990

For the failure detection of dynamic systems, processing the residuals from the observer of the estimator is the most general method. A failure detection method which use an adaptive predictor to separate the effect of sensor failure from the additive noise in the residuals of a Kalman filter that is employed as an estimator of a dynamic system is addressed here. In the method, the property of the residuals of an optimal Kalman estimator is exploited. The simulation results of this method shows that the proposed method is superior to the sequential probability ratio test for a small failure magnitude.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• 응용통계연구
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• 제16권2호
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• pp.239-246
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• 2003

소지역에서 직접(direct) 시계열추정을 할 수 있다면, 소지역들 추정에서 최적선형 불편 예측량(BLUP)을 일반화 시킬 수 있다. 특히 조사에서 얻어지는 관측 값의 오차가 시간상으로 상관관계가 있다면 Kalman Filter(KF)기법이 사용 될 수 있다. 이 연구는 예측 값을 활용한 소지역의 실업자 수 추정에서 표본으로 추출되지 않은, 즉 관측되지 않은 값의 예측모형에 KF기법을 적용하였다. 이는 경제활동인구수를 이용하여 현 시점의 소지역 실업자 수를 예측함수(BLUP)를 통해 추정하게 된다. 그리고 이를 단순 회귀분석 추정치와 비교하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.973-978
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• 2005

In this paper, we investigate an improved mobile robot localization method using Kalman filter. The highlight of the paper lies in the formulation of combined Kalman filter and its application to mobile robot experiment. The combined Kalman filter is a kind of extended Kalman filter which has an extra degree of freedom in Kalman filtering recursion. It consists of the standard Kalman filter, i.e., the predictor-corrector and the perturbation estimator which reconstructs unknown dynamics in the state transition equation of mobile robot. The combined Kalman filter (CKF) enables to achieve robust localization performance of mobile robot in spite of heavy perturbation such as wheel slip and doorsill crossover which results in large odometric errors. Intrinsically, it has the property of integrating the innovation in Kalman filtering, i.e., the difference between measurement and predicted measurement and thus it is so much advantageous in compensating uncertainties which has not been reflected in the state transition model of mobile robot. After formulation of the CKF recursion equation, we show how the design parameters can be determined and how much beneficial it is through simulation and experiment for a two-wheeled mobile robot under indoor GPS measurement system composed of four ultrasonic satellites. In addition, we discuss what should be considered and what prerequisites are needed to successfully apply the proposed CKF in mobile robot localization.

• 한국군사과학기술학회지
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• 제2권1호
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• pp.170-182
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• 1999

본 논문에서는 전자광학 추적 시스템의 영상추적부가 가지는 시간지연 특성을 보상하여 추적 성능을 향상할 수 있는 방법을 제시하였다. 제안된 방법은 Smith 예측기와 칼만 필터를 사용하여 시선의 시간지연 현상 및 표적의 기동정보 지연에 대한 보상을 가능하게하여 기존의 PI 또는 Smith 예측기만의 제어루프를 사용한 경우보다 추적 오차를 매우 줄일 수 있다. 제안된 방법의 타당성 확인을 위하여 실제 EOTS에 적용하여 다양한 모의실험 및 실험을 실시하여 그 성능 향상을 확인하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.478-481
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• 1996

Tracking a target of versatile maneuver recently demands a stable adaptation of tracker, and the multiple model techniques are being developed because of its ability to produce useful information of target maneuver. This paper presents the way to apply the multiple model method in a moving-target and moving-platform scenario, and the estimation and prediction results better than those of single Kalman filter.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.33-35
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• 2005

We propose an eye gaze tracking system under natural head movements. The system consists of one CCD camera and two front-surface mirrors. The mirrors rotate to follow head movements in order to keep the eye within the view of the camera. However, the mirror controller cannot guarantee the fast head movements, because the frame rate is generally 30Hz. To overcome this problem, we applied Kalman predictor to estimate next eye position from the current eye image. In the results, our system allows the subjects head to move 50cm horizontally and 40cm vertically, with the speed about 10cm/sec and 6cm/sec, respectively. And spatial gaze resolutions are about 4.5 degree and 4.5 degree, respectively, and the gaze estimation accuracy is 92% under natural head movements.

• 대한토목학회논문집
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• 제2권3호
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• pp.43-56
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• 1982

상태공간(狀態空間) 개념(槪念)에 기초(基礎)를 두어 시스템의 동적(動的) 거동(擧動)을 나타낸 Kalman filter와 자기공진(自己共振) 예측자(豫測子)의 순환(循還) 알고리즘에 의한 예측방법(豫測方法)을 연구(硏究)하여 하천유출(河川流出) 예측(豫測)에의 적용성(適用性)을 검토(檢討)하고 그 결과(結果)를 제시하였다. 강우(降雨)-유출과정(流出過程)의 동적(動的) 거동(擧動)을 자색(白色) Gaussian 잡음(雜音)이 있는 선형(線型), 이산형(離散型)시스템으로 보아서 낮은 차수(次數)의 ARMA 과정(過程)으로 나타내었으며 예측모형(豫測模型)의 상태(狀態)벡터를 random walk로 나타내었다. 예측오차(豫測誤差)에 대한 통계적(統計的)인 분석(分析)으로 모형구조(模型構造)를 결정하였으며 적용(適用)된 예측(豫測)알고리즘의 검정(檢正)을 위하여 시우량(時雨量)과 시유량(時流量)의 과거(過去) 기록치(記錄値)를 사용하였다. 예측결과(豫測結果)를 분석(分析)하나 Kalman filter에 의한 알고리즘이 자기공진(自己共振) 예측자(豫測子)보다 우수하다는 것을 알 수 있었다.