• Convergence Security Journal
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• v.8 no.4
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• pp.161-166
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• 2008

This paper proposes a new approach to align 3D data sets by using curvatures of feature surface. We use the Gaussian curvatures and the covariance matrix which imply the physical characteristics of the model to achieve registration of unaligned 3D data sets. First, the physical characteristics of local area are obtained by the Gaussian curvature. And the camera position of 3D range finder system is calculated from by using the projection matrix between 3D data set and 2D image. Then, the physical characteristics of whole area are obtained by the covariance matrix of the model. The corresponding points can be found in the overlapping region with the cross-projection method and it concentrates by removed points of self-occlusion. By the repeatedly the process discussed above, we finally find corrected points of overlapping region and get the optimized registration result.

• Wind and Structures
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• v.18 no.1
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• pp.37-56
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• 2014

The evaluation of pressure fields acting on slender structures under wind loads is currently performed in experimental aerodynamic tests. For wind-sensitive structures, in fact, the knowledge of global and local wind actions is crucial for design purpose. This paper considers a particular slender structure under wind excitation, representative of most common high-rise buildings, whose experimental wind field on in-scale model was measured in the CRIACIV boundary-layer wind tunnel (University of Florence) for several angles of attack of the wind. It is shown that an efficient reduced model to represent structural response can be obtained by coupling the classical structural modal projection with the so called blowing modes projection, obtained by decomposing the covariance or power spectral density (PSD) wind tensors. In particular, the elaboration of experimental data shows that the first few blowing modes can effectively represent the wind-field when eigenvectors of the PSD tensor are used, while a significantly larger number of blowing modes is required when the covariance wind tensor is used to decompose the wind field.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.64-69
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• 1993

In this paper we present the performance bounds of the optimal FIR filter in continuous time systems with modeling uncertainty. The performance measure bounds are calculated from the estimation error covariance bounds of the optimal FIR filter and the suboptimal FIR filter. Performance error bounds range are expressed by the upper bounds on the estimation error covariance difference between the real and nominal values in case of the systems with noise uncertainty or model uncertainty. The performance bounds of the systems are derived on the assumption that the system uncertainty and the estimation error covariance are imperfectly known a priori. The estimation error bounds of the optimal FIR filter is compared with those of the Kalman filter via a numerical example applied to the estimation of the motion of an aircraft carrier at sea, which shows the former has better performances than the latter.

• The Journal of the Acoustical Society of Korea
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• v.11 no.1
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• pp.67-79
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• 1992

For effective on-line ARMA parameter estimation, a covariance type ARMA fast transversal filter (FTF) algorithm is presented. The proposed algorithm is a covariance type implementation of ELS(Extended Least Squares) estimator and it is a fast time update recursion which is based on the fact that the correlation matrix of ARMA model satisfies the shift invariance property in each sub-block. The geometric approach is used in the derivation of the proposed algorithm. It takes small computational burden of 13N+37 MADPR(Multiplication And Division Per Recursion). Also, AR and MA orders can be independetly and arbitrarily specified.

• The Journal of the Acoustical Society of Korea
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• v.15 no.1
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• pp.34-39
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• 1996

Target state estimation is a fundamental problem of the sonar signal processing. In this paper, the covariance analysis techniques are applied to enhance the performance of the target state estimation of a monopulse sonar system. MOST, the artificial target signal generator based on the highlight model is used to generate signals in various target states. The performance of the developed method has been evaluated by applying it to the various S/N. The enhanced performance of the covariance analysis method presented in this paper is discussed.

• Structural Engineering and Mechanics
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• v.56 no.5
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• pp.767-785
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• 2015

The stochastic finite element method is employed to obtain a stochastic dynamic model of angled beams subjected to impact loads when uncertain material properties are described by random fields. Using the perturbation technique in conjunction with a precise time integration method, a random analysis approach is developed for efficient analysis of random elastic waves. Formulas for the mean, variance and covariance of displacement, strain and stress are introduced. Statistics of displacement and stress waves is analyzed and effects of bend angle and material stochasticity on wave propagation are studied. It is found that the elastic wave correlation in the angled section is the most significant. The mean, variance and covariance of the stress wave amplitude decrease with an increase in bend angle. The standard deviation of the beam material density plays an important role in longitudinal displacement wave covariance.

• Transactions on Electrical and Electronic Materials
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• v.17 no.1
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• pp.13-17
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• 2016

The ampere-hour integral method and the open circuit voltage method are integrated via the extended Kalman filter method so as to overcome insufficiencies of the ampere-hour integral method and the open circuit voltage method for estimating battery SOC. The process noise covariance and the measurement noise covariance of the extended Kalman filter method are simplified based on the Thevenin equivalent circuit model, with a proposed simplified SOC estimating method. Verification of DST experiments indicated that the battery SOC estimating method is simple and feasible, and the estimated SOC error is no larger than 2%.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.283-292
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• 1992

Updating algorithms are studied for the basic statistics (mean, variance). For a linear model, a recursive formulae for least squares estimators of regression coefficients, residual sum of squares and variance-covariance matrix are also studied. Hotelling's $T^2$ statistics can be calculated recursively using the recursive formulae of mean vector and variance-covariance matrix without computing the sample variance-covariance matrix at each stage.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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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.