• International Journal of Aeronautical and Space Sciences
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• v.1 no.1
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• pp.70-80
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• 2000

The attitude knowledge error model is formulated for specifically KOMPSAT attitude determination system using the Lefferts/Markley/Shuster method, and the attitude determination(AD) error analysis is performed so as to investgate the on-board attitude determination capability of KOrea Multi-Purpose SATellite(KOMPSAT) using the covariance analysis method. Analysis results show there is almost no initial value effect on Attitude Determination (AD) error and the sensor noise effects on AD error are drastically decreased as is predicted because of the inherent characteristic of Kalman filter structure. However, it shows that the earth radiance effect of IR-sensor(earth sensor) and the bias effects of both IR-sensor and fine sun sensor are the dominant factors degrading AD error and gyro rate bias estimate error in AD system. Analysis results show that the attitude determination errors of roll, pitch and yaw axes are 0.056, 0.092 and 0.093 degrees, respectively. These numbers are smaller than the required values for the normal mission of KOMPSAT. Also, the selected on-orbit data of KOMPSAT is presented to demonstrate the designed AD system.

• Atmosphere
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• v.23 no.1
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• pp.33-37
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• 2013

In meteorology, exploitation of Kalman filter as a data assimilation system is virtually impossible due to simultaneous requirements of adjoint model and large computer resource. The other substitute of utilizing ensemble Kalman filter is only affordable by compensating an enormous usage of computing resource. Furthermore, the latter employs ensemble integration sets for evolving the background error covariance matrix by compensating the dynamical feature of the temporal evolution of weather conditions. We propose a new implementation method that works without the adjoint model by utilizing the explicit expression of the background error covariance matrix in backward evolution. It will also break a barrier in the evolution of the covariance matrix. The method may be applied with a slight modification to the real time assimilation or the retrospective analysis.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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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.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.103-117
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• 2017

In longitudinal data analysis, the serial correlation of repeated outcomes must be taken into account using covariance matrix. Modeling of the covariance matrix is important to estimate the effect of covariates properly. However, It is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcome the restrictions, several Cholesky decomposition approaches for the covariance matrix were proposed: modified autoregressive (AR), moving average (MA), ARMA Cholesky decompositions. In this paper we review them and compare the performance of the approaches using simulation studies.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.117-120
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• 1996

The objective of this study is to suggest modified Covariance Structure Analysis that combine with existing Multivariate Statistical Method which is used Casual Analysis Method in Management Information. For this purpose, we'll consider special feature and limitation about Correlation Analysis, Regression Analysis, Path Analysis and connect Covariance Structure Analysis with Statistical Factor Analysis so that theoretical casual model compare with variables structure in collecting data. A example is also presented to show the practical applicability of this approach.

• Journal of the Korea Society of Computer and Information
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• v.15 no.5
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• pp.13-21
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• 2010

The Optimal results of Kalman Filtering on the Inverted Pendulum System requires an effective factor such as the noise covariance matrix Q, the measurement noise covariance matrix R and the initial error covariance matrix $P_0$. We present a special case where the optimality of the filter is not destroyed and not sensitive to scaling of these covariance matrix because these factors are unknown or are known only approximately in the practical situation. Moreover, the error covariance matrices issued by this method predict errors in the state estimate consistent with the scaled covariance matrices and not the issued state estimates. Various results using the scalar gain $\delta$ are derived to described the relations among the three covariance matrices, Kalman Gain and the error covariance matrices. This paper is described as follows: Section III a brief overview of the Inverted Pendulum system. Section IV deals with the mathematical dynamic model of the system used for the computer simulation. Section V presents a various simulation results using the scalar gain.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1348-1351
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• 1997

The federated Kaman filter(FKF) is being widely used in many multisensor navigatiion systems. It is know that the FKF has advantages of simplicity and fault-tolerance over other decentralized filter techniques. In this paper, optimal and suboptimal FKF configuratiions are mentioned and a covariance analysis technique for the suboptimal FKF is newly presented. The suboptimal FKF configuration, known as No-reset(NR) mode, has better fault tolerance capability than the optimal FKF coniguratioin. In the suggested technique, a suboptimal fusion process of FKF is considered a swell as suboptimal gains of local filters. An upper boun of error covariance for suboptimal FKF is derived. Also, it is mathematically shown that this bound is smaller thanexisting bound in the literatrue. A vehicle-navigaion system is designed using the FKF. In thissystem, a map constraing equation is introduced and used as a measurement equatioin of Kalman filter. Performance analysis is done by the suggested covariance analysis techniques.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.7
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• pp.563-568
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• 2003

In this paper, We propose a target tracking filter which utilizes the PDA for data association in a clutter environment and also propose an analytic solution for ideal filter covariance which accounts for all the possible events in the PDA. Monte Carlo simulation for the proposed filter in a clutter environment indicates that the proposed analytic solution forms the true error covariance of the PDA Filter.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.755-771
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• 2014

Isotropy is one of the main assumptions for the ease of spatial prediction (named kriging) based on some covariance models. A lack of isotropy (or anisotropy) in a spatial process necessitates that some additional parameters (angle and ratio) for anisotropic covariance model be obtained in order to produce a more reliable prediction. In this paper, we propose a new class of geometrically extended anisotropic covariance models expressed as a weighted average of some geometrically anisotropic models. The maximum likelihood estimation method is taken into account to estimate the parameters of our interest. We evaluate the performances of our proposal and compare it with an isotropic covariance model and a geometrically anisotropic model in simulation studies. We also employ extended geometric anisotropy to the analysis of real data.

• Communications for Statistical Applications and Methods
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• v.19 no.5
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• pp.721-729
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• 2012

Various methods control the influence of a covariate on a response variable. These methods are analysis of covariance(ANCOVA), RANK ANCOVA, ANOVA of (covariate-adjusted) residuals, and Kruskal-Wallis tests on residuals. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set that ignore the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. In this paper, we proposed the new nonparametric method on the ANCOVA model, as applying joint placement in a one-way layout on residuals as described in Chung and Kim (2007). A Monte Carlo simulation study is adapted to compare the power of the proposed procedure with those of the previous procedure.