• 품질경영학회지
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• 제29권1호
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• pp.1-10
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• 2001

We consider the problem of detecting special variations in multivariate $T^2$-control chart when two or more multivariate outliers are present. Since a multivariate outlier may reflect slippage in mean, variance, or correlation, it can distort the sample mean vector and sample covariance matrix. Damaged sample mean vector and sample covariance matrix have difficulty in examining special variations clearly, An alternative to detection outliers or special variations is to use robust estimators of mean vector and covariance matrix that are less sensitive to extreme observations than are the standard estimators $\bar{x}$ and $\textbf{S}$. We applied popular minimum volume ellipsoid(MVE) and minimum covariance determinant(MCD) method to estimate mean vector and covariance matrix and compared its results with standard $T^2$-control chart using simulated multivariate data with outliers. We found that the modified $T^2$-control chart based on the above robust methods were more effective in detecting special variations clearly than the standard $T^2$-control chart.

• International Journal of Aeronautical and Space Sciences
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• 제18권3호
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• pp.555-564
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• 2017

In this paper, a new technique for attitude and heading reference system (AHRS) using low-cost MEMS sensors of the gyroscope, accelerometer, and magnetometer is addressed particularly in vibration environments. The motion of MEMS sensors interact with the scale factor and cross-coupling errors to produce random errors by the harsh environment. A new adaptive attitude estimation algorithm based on the Kalman filter is developed to overcome these undesirable side effects by analyzing windowed measurement error covariance. The key idea is that performance degradation of accelerometers, for example, due to linear vibrations can be reduced by the proposed measurement error covariance analysis. The computed error covariance is utilized to the measurement covariance of Kalman filters adaptively. Finally, the proposed approach is verified by using numerical simulations and experiments in an acceleration phase and/or vibrating environments.

• 한국음향학회지
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• 제33권5호
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• pp.326-334
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• 2014

논문은 MVDR 빔 형성 기법을 위한 새로운 공분산 행렬 예측을 제안한다. 오디오-비디오 센서를 이용하여 음원의 방향 정보를 정확히 추적함으로써, 공분산 행렬은 가변 적응 망각율을 적용하여 효과적으로 예측된다. 가변 적응 망각율은 신호 대 방해 신호 비를 고려하여 결정된다. 실험 결과에서는 제안하는 방법의 성능이 방해신호/잡음 감소 및 음성 왜곡의 면에서 기존의 방법의 성능보다 더 우수하다는 것을 보여준다.

• 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.

• 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.

• 전자공학회논문지SC
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• 제39권2호
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• pp.166-175
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• 2002

이 논문에서는 표적 추적에 사용되는 PSN(Probabilistic Strongest Neighbor) 필터의 추적 성능을 예측한다. PSN 필터는 가장 강한 신호 크기를 가진 측정이 표적이외의 것으로부터 발생할 수 있다는 사건을 충분히 고려하기 때문에, 추적 성능에서 뿐만 아니라, 계산량 측면에서도 PDA(Probabilistic Data association) 필터보다 뛰어나다고 알려져 있다. 추적필터의 추정오차 공분산행렬(covariance matrix)은 추적의 성능을 결정하는 성능지수(performance index)로 널리 사용된다. PSN 필터의 추정오차 공분산행렬은 측정 데이터의 함수로써, 측정 데이터와 무관하게 추적기의 성능을 표현하기 위해서 HYCA(HYbrid Conditional Average)방법을 이용하여 추정오차 공분산행렬의 기대값에 대한 식을 제시하였다. 수치실험을 통하여 이 논문에서 제시한 성능 예측이 타당함을 보인다.

• 한국전자파학회논문지
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• 제21권1호
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• pp.20-27
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• 2010

본 논문은 space-time signal processing(STAP)의 신호 모델과 불균일한 클러터 환경을 제시하고 이를 극복하고자 연구된 nonhomogeneity detector(NHD)의 사용 이후에도 일어나는 심각한 성능 저하를 극복하고자 중위수(median)를 이용한 간섭 공분산 행렬의 새로운 예측 방법을 제시하였다. 또한 대각 로드(diagonal loading)를 적용하여 고유값(eigen value) 특성을 개선시켰으며 signal to interference plus noise ratio(SINR) 손실을 계산하여 비교하였다. 마지막으로 modified sample matrix inversion(MSMI) 통계량으로 목표물 검출 능력을 비교한 결과 제시된 방법이 평균값(average)으로 정의된 기존의 방법보다 우수한 성능을 보임을 확인하였다.

• 한국전자파학회논문지
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• 제29권7호
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• pp.560-565
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• 2018

기존에 제안된 SPICE(Sparse Iterative Covariance-based Estimation) 알고리즘은 시간영역에서 구현되며 공분산 행렬에 sparse recovery 기법을 적용함으로써 표적 방위각을 추정한다. 본 논문은 기존의 시간영역에서 다루던 SPICE 알고리즘을 주파수 영역으로 확장함으로써 주파수 영역에서도 구현 가능함을 보여준다. 또한 기존의 주파수 영역 표적 방위각 추정 알고리즘의 성능에 비하여 제안된 알고리즘의 성능이 우수함을 보여준다.

• 응용통계연구
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• 제26권5호
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• pp.747-758
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• 2013

공분산 행렬은 다변량 통계분석에서 중요한 역할을 하고 있으며 전통적인 다변량 분석의 경우 표본 공분산 행렬이 참공분산 행렬의 추정량으로 주로 사용되었다. 하지만 변수의 수가 표본의 크기보다 훨씬 큰 고차원 데이터와 같은 경우에는 표본 공분산 행렬은 비정칙행렬이 되어 기존의 다변량 기법을 사용하는 데 적절하지 않을 수가 있다. 최근 이러한 문제점을 해결하기 위해 축소추정, 경계추정, 수정 콜레스키 분해 추정 등의 새로운 공분산 행렬의 추정량들이 제안되었다. 본 논문에서는 추정량들의 성능에 영향을 미칠 수 있는 여러 현실적인 상황들을 가정하여 모의실험을 통해 참공분산 행렬의 추정량들의 성능을 비교하였다.

• Communications for Statistical Applications and Methods
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• 제3권2호
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• pp.37-42
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• 1996

Maximum likelihood estimation of Cholesky decomposition is considered under normality assumption. It is shown that maximum liklihood estimation gives a Cholesky decomposition of the sample covariance matrix. The joint distribution of the maximum likelihood estimators is derived. The ussual algorithm for a Cholesky decomposition is shown to be equivalent to a maximumlikelihood estimation of a Cholesky root when the underlying distribution is a multivariate normal one.