• 한국항공우주학회지
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• 제30권8호
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• pp.78-86
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• 2002

GPS 반송파 위상관측값은 미지정수를 포함하고 있어 이를 효과적으로 해결하기 위해 분산-공분산 행렬의 비상관화 과정이 필요하다. 본 논문에서는 새로운 미지정수 비상관화 방법을 제시하고자 한다. 이 방법은 분산-공분산 행렬을 유사한 크기의 작은 4개 블록으로 나눈 다음 각 블록을 독립적으로 비상관화 하는 것이다. 각 블록의 비상관화는 전 단계의 결과가 다음 단계의 과정에 영향을 받지 않도록 귀납적으로 이루어진다. 임의로 선정된 몇가지 수치적 예시에 의하면 이 방법은 기존의 다른 방법보다 좋거나 유사한 결과를 보여 주지만, 분산-공분산 행렬의 작은 블록에서 계산이 이루어지므로 본 방법의 계산 속도가 상대적으로 빠르다.

• 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.

• 제어로봇시스템학회논문지
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• 제12권2호
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• 한국정보통신학회논문지
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• 제7권3호
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• pp.437-447
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• 2003

Circulant Matrix Factorization (CMF)는 covariance 행렬의 spectral factorization된 결과를 얻을 수 있다. 우리는 얻어진 결과를 가지고 일반적으로 잘 알려진 방법인 Schur algorithm을 이용하여 finite impulse response(FIR)와 infinite impulse response (IIR) lattice 필터를 설계하는 방법을 제안하였다. CMF는 기존에 많이 사용되는 root finding을 사용하지 않고 covariance polynomial로부터 minimum phase 특성을 가지는 polynomial을 얻는데 유용한 방법이다. 그리고 Schur algorithm은 toeplitz matrix를 빠르게 Cholesky factorization하기 위한 방법으로 이 방법을 이용하면 FIR/IIR lattice 필터의 계수를 쉽게 찾아낼 수 있다. 본 논문에서는 이러한 방법들을 이용하여 FIR과 IIR lattice 필터의 설계의 계산적인 예제를 제시했으며, 제안된 방법과 다른 기존에 제시되었던 방법 (polynomial root finding과 cepstral deconvolution)들과 성능을 비교 평가하였다.

• 대한전자공학회논문지
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• 제21권4호
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• pp.7-12
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• 1984

DFT로 대각선화 할 수 있는 circulant matrix가 대칭이고 실수인 경우에 이를 대각선화 할 수 있는 CDFT(composite DFT)를 유도했다. 일반적인 실수 신호의 대칭 covariance matrix에 대하여 DFT와 CDFT 변환했을 경우의 variance 분포를 분석했고, 이를 토대로 rate distortion 이론에 의하여 이들의 성능을 비교한 결과 CDFT가 DFT보다 bit rate면에서 효과적임을 볼 수 있었다. 그리고 f(q)=(0.95)q인 covariance matrix(64×64)에 대해 CDFT가 DFT에 비해. 계산결과, 평균적으로 0.0095bit가 감소될 수 있었다.

• Asian-Australasian Journal of Animal Sciences
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• 제22권7호
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• pp.931-936
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• 2009

Body weights of 862 Angora goats between birth and 36 months of age, recorded on a semiyearly basis from 1988 to 2000, were used to estimate genetic, permanent environmental and phenotypic covariance functions. These functions were estimated by fitting a random regression model with 6th order polynomial for direct additive genetic and animal permanent environmental effects and 4th and 5th order polynomial for maternal genetic and permanent environmental effects, respectively. A phenotypic covariance function was estimated by modelling overall animal and maternal effects. The results showed that the most variable coefficient was the intercept for both direct and maternal additive genetic effects. The direct additive genetic (co)variances increased with age and reached a maximum at about 30 months, whereas the maternal additive genetic (co)variances increased rapidly from birth and reached a maximum at weaning, and then decreased with age. Animal permanent environmental (co)variances increased with age from birth to 30 months with lower rate before 12 months and higher rate between 12 and 30 months. Maternal permanent environmental (co)variances changed little before 6 months but then increased slowly and reached a maximum at about 30 months. These results suggested that the contribution of maternal additive genetic and permanent environmental effects to growth variation differed from those of direct additive genetic and animal permanent environmental effects not only in expression time, but also in action magnitude. The phenotypic (co)variance estimates increased with age from birth to 36 months of age.

• 한국자동차공학회논문집
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• 제14권2호
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• pp.23-31
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• 2006

Tolerance analysis of auto body requires the consideration of its compliance because of potentially significant deformation during the spot-weld assembly process. In this paper, a relatively recent method for such analyses is briefly introduced as one can find in the literature. In this method, it is important to take into account of the covariance between the sources of variation as they are closely located, which is the case in most auto body assembly. However, it is often impossible to know such covariance, for example, when a new car is being developed. Therefore, a mechanics-based method is proposed in this paper to estimate the covariance among the sources of variation by finite element analyses and simple statistical computations. The proposed method is illustrated by applying it to a three-dimensional model of real front wheel housing.

• International Journal of Aeronautical and Space Sciences
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• 제11권4호
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Wind and Structures
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• 제16권5호
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• pp.517-540
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• 2013

This paper presents applications of proper orthogonal decomposition in both the time and frequency domains based on both cross spectral matrix and covariance matrix branches to analyze multi-variate unsteady pressure fields on prisms and to study spanwise and chordwise pressure distribution. Furthermore, modification of proper orthogonal decomposition is applied to a rectangular spanwise coherence matrix in order to investigate the spanwise correlation and coherence of the unsteady pressure fields. The unsteady pressure fields have been directly measured in wind tunnel tests on some typical prisms with slenderness ratios B/D=1, B/D=1 with a splitter plate in the wake, and B/D=5. Significance and contribution of the first covariance mode associated with the first principal coordinates as well as those of the first spectral eigenvalue and associated spectral mode are clarified by synthesis of the unsteady pressure fields and identification of intrinsic events inside the unsteady pressure fields. Spanwise coherence of the unsteady pressure fields has been mapped the first time ever for better understanding of their intrinsic characteristics.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.259-264
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• 2006

It is well known that the uncertainty of the covariance parameters of the process noise (Q) and the observation errors (R) has a significant impact on Kalman filtering performance. Q and R influence the weight that the filter applies between the existing process information and the latest measurements. Errors in any of them may result in the filter being suboptimal or even cause it to diverge. The conventional way of determining Q and R requires good a priori knowledge of the process noises and measurement errors, which normally comes from intensive empirical analysis. Many adaptive methods have been developed to overcome the conventional Kalman filter's limitations. Starting from covariance matching principles, an innovative adaptive process noise scaling algorithm has been proposed in this paper. Without artificial or empirical parameters to be set, the proposed adaptive mechanism drives the filter autonomously to the optimal mode. The proposed algorithm has been tested using road test data, showing significant improvements to filtering performance.