• 센서학회지
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• 제29권6호
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• pp.440-446
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• 2020

A well-known difficulty in attitude estimation based on inertial measurement unit (IMU) signals is the occurrence of external acceleration under dynamic motion conditions, as the acceleration significantly degrades the estimation accuracy. Lee et al. (2012) designed a Kalman filter (KF) that could effectively deal with the acceleration issue. Ahmed and Tahir (2017) modified this method by adjusting the acceleration-related covariance matrix because they considered covariance modeling as a pivotal factor in the estimation accuracy. This study investigates the effects of covariance modeling on estimation accuracy in an IMU-based attitude estimation KF. The method proposed by Ahmed and Tahir can be divided into two: one uses the covariance including only diagonal components and the other uses the covariance including both diagonal and off-diagonal components. This paper compares these three methods with respect to the motion condition and the window size, which is required for the methods by Ahmed and Tahir. Experimental results showed that the method proposed by Lee et al. performed the best among the three methods under relatively slow motion conditions, whereas the modified method using the diagonal covariance with a high window size performed the best under relatively fast motion conditions.

• 한국경영과학회지
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• 제39권4호
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• pp.137-152
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• 2014

In this paper, I suggest several techniques to estimate covariance matrix and compare the performance of the global minimum variance portfolio (GMVP) in terms of out of sample mean standard deviation and return. As a result, the return differences among the GMVPs are insignificant. The mean standard deviation of the GMVP using historical covariance is sensitive to the estimation window and the number of assets in the portfolio. Among the model covariance, the GMVP using constant systematic risk ratio model or using short sale restriction shows the best performance. The performance difference between the GMVPs using historical covariance and model covariance becomes insignificant as the historical covariance is estimated with longer estimation window. Lastly, the implied volatilities from ELW prices do not lead to superior performance to the historical variance.

• 디지털산업정보학회논문지
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• 제10권4호
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• pp.53-61
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• 2014

In this paper, we proposed covariance weight matrix using SPT matrix in order to accurate target estimation. We have estimated a target using modified covariance matrix and beam steering error method. We have minimized beam steering error in order to estimation desired a target. This method obtain optimum covariance weight using modified SPT matrix. This paper of proposal method is showed good performance than general method. We updated a weight of covariance matrix using modified SPT matrix. We obtain optimum covariance matrix weight to application beam steering error method in order to beam steering toward desired target. Through simulation, we showed that compare proposal method with general method. It have improved resolution of estimation target to good performance more proposed method than general method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1933-1938
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• 2003

A covariance matrix is a tool that expresses odometry uncertainty of the wheeled mobile robot. The covariance matrix is a key factor in various localization algorithms such as Kalman filter, topological matching and so on. However it is not easy to acquire an accurate covariance matrix because we do not know the real states of the robot. Up to the authors knowledge, there seems to be no established result on the covariance matrix estimation for the odometry. In this paper, we propose a new method which can estimate the covariance matrix from empirical data. It is based on the PC-method and shows a good estimation ability. The experimental results validate the performance of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.264-267
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• 1991

In this paper, a new application method of the Kalman filter to desigin a gyro is proposed. The role of a gyro is the estimation of an input rate with minimal error covariance. The size of the error covariance depends on gyro's parameters, which makes it possible to use the parameters of gyro to minimze the estimation error covariance. Numerical analysis shows that the error covariance becomes smaller as the spin axis momentum becomes larger and the damping coefficient smaller, but production cost must be considered. Through numerical analysis the parameter set for an acceptable - performance gyro with small cost can be selected.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.449-451
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• 2003

The estimation of time-series data is fundamental process in many data analysis cases. However, the unwanted measurement error is usually added to true data, so that the exact estimation depends on efficient method to eliminate the error components. The wavelet transform method nowadays is expected to improve the accuracy of estimation, because it is able to decompose and analyze the data in various resolutions. Therefore, the wavelet based Kalman filter method for the estimation of time-series data is proposed in this paper. The wavelet transform separates the data in accordance with frequency bandwidth, and the detail wavelet coefficient reflects the stochastic process of error components. This property makes it possible to obtain the covariance of measurement error. We attempt the estimation of true data through recursive Kalman filtering algorithm with the obtained covariance value. The procedure is verified with the fundamental example of Brownian walk process.

• Communications for Statistical Applications and Methods
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• 제20권3호
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• pp.235-240
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• 2013

Generalized linear mixed models(GLMMs) are frequently used for the analysis of longitudinal categorical data when the subject-specific effects is of interest. In GLMMs, the structure of the random effects covariance matrix is important for the estimation of fixed effects and to explain subject and time variations. The estimation of the matrix is not simple because of the high dimension and the positive definiteness; subsequently, we practically use the simple structure of the covariance matrix such as AR(1). However, this strong assumption can result in biased estimates of the fixed effects. In this paper, we introduce Bayesian modeling approaches for the random effects covariance matrix using a modified Cholesky decomposition. The modified Cholesky decomposition approach has been used to explain a heterogenous random effects covariance matrix and the subsequent estimated covariance matrix will be positive definite. We analyze metabolic syndrome data from a Korean Genomic Epidemiology Study using these methods.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.445-450
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• 2018

Reliable detection and parameter estimation of a radar cross section(RCS) fluctuating target have been known as a difficult task. To reduce the effect of RCS fluctuation, various diversity techniques have been considered. This paper presents a new method for synthesizing a covariance matrix applicable to a coherent multi-input multi-output(MIMO) radar with frequency diversity. It is achieved by efficiently combining covariance matrices corresponding to different carrier frequencies such that the signal-to-noise ratio(SNR) in the combined covariance matrix is maximized. The value of a synthesized covariance matrix is assessed by examining the phase curves of its entries and the improvement on direction of arrival(DOA) estimation.

• 대한전자공학회논문지TC
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• 제40권1호
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• pp.15-23
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• 2003

본 논문에서는 입사 신호의 근사 공분산 행렬을 이용하여 신호의 입사각을 빠르게 추정하는 입사각 추정 알고리듬을 제안한다. MUSIC(MUltiple Signal Classification) 알고리듬과 같은 기존의 부분공간 입사각 추정 알고리듬은 입력 공분산 행렬을 구하기 위해서 다수의 표본 신호를 필요로 하며, 입력 공분산 행렬을 획득하기 위한 표본 신호의 수신시간 동안 입사각 추정이 수행될 수 없으므로 빠른 신호처리가 불가능하다. 또한 코히어런트 신호가 입사하는 경우에 코히어런트 신호간의 간섭으로 신호의 입사각을 정확하게 추정할 수 없다. 제안한 입사각 추정 알고리듬은 빔 형성기를 이용하여 매 표본 신호의 공간적인 빔 형성을 먼저 수행하여 신호간의 간섭을 제거한 후에 센서의 출력 값을 이용하여 방위각 응답(bearing response)과 방향 스펙트럼(directional spectrum)을 구한다. 방위각 응답으로 대략적인 신호의 입사각을 추정한 후에 방향 스펙트럼을 이용하여 정착하게 신호의 입사각을 추정한다. 제안 입사각 추정 알고리듬은 공분산 행렬을 구하기 위하여 그 순간의 각 어레이 소자에 입사되는 표본 신호만을 사용하고 방위각 응답을 구하기 위해서 몇 순간 동안의 표본 신호만 필요로 하므로 기존 입사각 추정 알고리듬에 비하여 크게 향상된 입사각 추정 속도를 갖는다.

• 한국통신학회논문지
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• 제22권7호
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• pp.1534-1542
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• 1997

In this paper, we present a performance improvement method for the direction-of-arrival (DOA) estimation algorithm of the narrowband signals incident on a uniform circular array. It is very important to estimate the covariance matrix effectively because the performance of DOA algorithm mainly depends on the exactness of the sampel coveriance matrix which is computed from the received samples of signals. In case of uniform circular array with the even number sensors, the structure of the arrray has a useful geometrical property. Therefore we present the method which can estimate covariance matrix more effectively using this property. The simulation results are shown to demonstrate the superior perfodrmance obtained by the proposed covariance matrix estimation method relative to that of the conventional estimation method.