• 한국게임학회 논문지
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• 제12권3호
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• pp.87-96
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• 2012

본 논문에서는 시퀀스 상에서 확장 칼만필터(Extended Kalman Filter) 기반의 SLAM(Simultaneous Localization And Mapping) 시스템의 안정적인 카메라 추적과 재위치(re-localization) 방법이 제안된다. SLAM으로 얻어진 3차원 특징점에 들로네(Delaunay) 삼각화를 적용하여 기준(reference) 평면을 설정하며, 평면상에 존재하는 특징점의 BRISK(Binary Robust Invariant Scalable Keypoints) 기술자(descriptor)를 생성한다. 기존 확장 칼만필터의 오차가 누적되는 경우를 판단하여 기준 평면의 호모그래피로부터 카메라 정보를 해석한다. 또한 카메라가 급격하게 이동해서 특징점 추적이 실패하면, 저장된 강건한 기술자 정보를 매칭하여 카메라의 위치를 다시 추정한다.

• 대한조선학회논문집
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• 제40권5호
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• pp.43-52
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• 2003

Two methods which estimate manoeuvring derivatives in the model of hydrodynamic force and moment acting on a manoeuvring ship using sea trial data were compared. One is the widely used parameter estimation method by using the Extended Kalman Filter (EKF), which estimates state variables of linearized state space model at every instant after dealing with the coefficients as the augmented state variables. The other one is the Estimation-Before-Modeling (EBM) technique, so called the two-step method. In the first step, hydrodynamic force of which dynamic model is assumed the third-order Gauss-Markov process is estimated along with motion variables by the EKF and the modified Bryson-Frazier smoother. Then, in the next step, manoeuvring derivatives are identified through the regression analysis. If the exact structure of hydrodynamic force could be known, which was an ideal case, the EKF method would be regarded as being more superior compared to the EBM technique. However the EBM technique was more robust than the EKF method from a realistic point of view where the assumed model structure was slightly different from the real one.

• 한국조명전기설비학회지:조명전기설비
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• 제9권6호
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• pp.41-48
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• 1995

본 연구에서는 Extended Kalman Filter(EKF)를 이용한 속도센서없는 우도전동기의 벡터제어의 구현을 제안하였다. 또한 회전자 저항의 변동을 보상 할 수 있도록 회전자 저항도 추정한다. 이산화된 유도전동기의 모델을 통해 유도 전동기의 속도와 회전자 저항을 포함한 상태 변수를 정의하고 벡터 제어에 필요한 자속각을 추정하여 노이즈 환경에 놓인 시스템의 동작 특성을 안정되게 하였다. EKF알고리즘의 연산을 위하여 DSP를 이용하고, 전류제어 장치로 공간 전압벡터 변조 방식의 적용이 용이한 마이크로 콘트롤러를 체용하고, 인버터는 IPM(Intelligent Power Module)으로 실험 장치를 구성하였다. 시뮬레이션과 실험을 통하여 속도 추정 특성과 회전자 저항 특성을 살펴본 결과, 본 논문의 EFK 알고리즘이 속도 센서없는 유도전동기 벡터제어에 적합함을 입증할 수 있었다.

• International Journal of Aeronautical and Space Sciences
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• 제18권1호
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• pp.118-128
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• 2017

In this study, we propose a real time inertial navigation system/global positioning system (INS/GPS) integrated smoothing algorithm based on an extended Kalman filter (EKF) and a position damping loop (PDL) for synthetic aperture radar (SAR). Integrated navigation algorithms usually induce discontinuities due to error correction update by the Kalman filter, which are as detrimental to the performance of SAR as the relative position error. The proposed smoothing algorithm suppresses these discontinuities and also reduces the relative position error in real time. An EKF estimates the navigation errors and sensor biases, and all the errors except for the position error are corrected directly and instantly. A PDL activated during SAR operation period imposes damping effects on the position error estimates, where the estimated position error is corrected smoothly and gradually, which contributes to the real time smoothing and small relative position errors. The residual errors are re-estimated by the EKF to maintain the estimation performance and the stability of the overall loop. The performance improvements were confirmed by Monte Carlo simulations. The simulation results showed that the discontinuities were reduced by 99.8% and the relative position error by 48% compared with a conventional EKF without a smoothing loop, thereby satisfying the basic performance requirements for SAR operation. The proposed algorithm may be applicable to low cost SAR systems which use a conventional INS/GPS without changing their hardware configurations.

• 한국항행학회논문지
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• 제21권1호
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• pp.138-144
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• 2017

본 논문에서는 INS/GPS결합 시스템에서 GPS가 기만신호 또는 지형지물에 의한 가시선이 제한되어 위성의 기하학적 배치가 저하되는 조건을 고려하였고, 통합항법 성능을 향상시키기 위한 방법을 제안하였다. 먼저 GPS의 DOP에 측정 공분산 이 연동되는 가변 공분산 확장 칼만필터(VCEKF)를 제시하였다. 그리고 몬테칼로 시뮬레이션을 통하여 EKF와 VCEKF를 사용한 통합항법 시스템의 항법성능을 분석하였다. DOP 값이 낮은 경우보다 DOP값이 높을 경우에 VCEKF가 확정 공분산을 사용하는 EKF보다 우수한 추정 성능을 보임을 확인할 수 있었다.

• 한국정밀공학회지
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• 제32권5호
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• pp.471-481
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• 2015

This paper presents the robot vision control schemes using Extended Kalman Filter (EKF) method for the slender bar placement in the appearance of obstacles during robot movement. The vision system model used for this study involves the six camera parameters($C_1{\sim}C_6$). In order to develop the robot vision control scheme, first, the six parameters are estimated. Then, based on the estimated parameters, the robot's joint angles are estimated for the slender bar placement. Especially, robot trajectory caused by obstacles is divided into three obstacle regions, which are beginning region, middle region and near target region. Finally, the effects of number of obstacles using the proposed robot's vision control schemes are investigated in each obstacle region by performing experiments of the slender bar placement.

• Structural Monitoring and Maintenance
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• 제3권2호
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• pp.115-127
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• 2016

It is a challenging problem of assessing the location and extent of structural damages with vibration measurements. In this paper, an improved Extended Kalman filter (EKF) with Tikhonov regularization is proposed to identify structural damages. The state vector of EKF consists of the initial values of modal coordinates and damage parameters of structural elements, therefore the recursive formulas of EKF are simplified and modal truncation technique can be used to reduce the dimension of the state vector. Then Tikhonov regularization is introduced into EKF to restrain the effect of the measurement noise for improving the solution of ill-posed inverse problems. Numerical simulations of a seven-story shear-beam structure and a simply-supported beam show that the proposed method has good robustness and can identify the single or multiple damages accurately with the unknown initial structural state.

• 제어로봇시스템학회논문지
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• 제22권7호
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• pp.562-569
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• 2016

This paper proposes a navigation system with a robust localization method for an underwater unmanned vehicle. For robust localization with IMU (Inertial Measurement Unit), a DVL (Doppler Velocity Log), and depth sensors, the EKF (Extended Kalman Filter) has been utilized to fuse multiple nonlinear data. Note that the GPS (Global Positioning System), which can obtain the absolute coordinates of the vehicle, cannot be used in the water. Additionally, the DVL has been used for measuring the relative velocity of the underwater vehicle. The DVL sensor measures the velocity of an object by using Doppler effects, which cause sound frequency changes from the relative velocity between a sound source and an observer. When the vehicle is moving, the motion trajectory to a target position can be recorded by the sensors attached to the vehicle. The performance of the proposed navigation system has been verified through real experiments in which an underwater unmanned vehicle reached a target position by using an IMU as a primary sensor and a DVL as the secondary sensor.

• 한국항행학회논문지
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• 제15권4호
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• pp.536-542
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• 2011

본 논문에서는 GPS/INS 강결합 시스템에서 다중경로 오차에 대한 EKF와 total-state UKF 두 가지 필터의 성능을 비교 분석하였다. 도심 빌딩숲에서 많이 발생하는 다중경로 오차가 없는 경우와 있는 경우에 대하여 시뮬레이션을 수행하였고, 위성이 3개만 보이는 경우에 두 필터의 추정 성능을 비교하였다. 다중경로 오차가 없을 경우보다 있는 경우에 total-state UKF가 EKF에 비하여 더 좋은 추정 성능을 보여서 total-state UKF의 성능이 더 강인함을 확인할 수 있었다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 춘계 학술대회 학술발표 논문집
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• pp.303-306
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• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.