• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.123-126
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• 1991

A generalization of null-spectrum for use in the estimation of directions of arrival of signal sources is considered in this paper. The upper and lower bounds of the generalized null-spectrum, the maximum and minimum null-spectra, are also derived. We observed that the maximum null-spectrum has higher resolution capability than other null-spectra including the two well-known null-spectra, the multiple signal classification null-spectrum and the Min-Norm null-spectrum.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.529-531
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• 2005

A block-based fast motion estimation algorithm is proposed in this paper to perform motion estimation based on the efficiently reduced search ranges in MPEG-4(ERS). This algorithm divides the search areas into several small search areas and the candidate small search area that has the lowest average of sum norm difference between current macroblock and candidate macroblock is chosen to perform block motion estimation using the Nobel Successive Elimination Algorithm (NSEA). Experimental results of the proposed algorithm show that the averaging PSNR improvement is better maximum 0.125 dB than other tested algorithms and bit saving effect is maximum 20kbps for some tested sequences in low-bit rate circumstance.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.10 no.6
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• pp.959-967
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• 1999

In this paper, we propose a Linear Prediction Method(LPM) in conjunction with signal enhancement for solving the direction-of-arrival estimation problem of multiple incoherent plane waves incident on a uniform linear array. The basic idea of signal enhancement is that of finding the covariance matrix of given rank that lies closest to a given estimated matrix in Frobenius norm sense. It is well known that LPM has a high-resolution performance in general applications, while it provides a lower statistical performance in lower SNR environment. To solve this problem, the LPM combined with signal enhancement approach is herein proposed. Simulation results are illustrated to demonstrate the better performance of the proposed method than conventional LPM.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1469-1472
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• 1997

For system state estimation, existing LMS type esimators widely used. For example Kalman filter is one of them. In this paper, a state estimator is derived for the H$_{\infty}$ norm of the estimation error spectrum matrix to be minimized. For the solution of this problem classical NP interpolation problem is used. Also, by deriving the duality between the filter problem and the well-known H$_{\infty}$ control problem, another solution is obtained. The computer simuation results show that H$_{\infty}$ estimator has less estimation error and so this is better than the existing Kalman filter estimator.or.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.3
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• pp.243-248
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• 2010

This study addresses spacecraft moment of inertial estimation approach using Modified Rodrigues Parameters(MRP). The MRP offer advantage by avoiding singularity in Kalman Filter design for attitude determination caused by the norm constraint of quaternion parameters. Meanwhile, MRP may suffer singularity for large angular displacement, so that we designed appropriate reference attitude motion for accurate estimation. The proposed approach is expected to provide stable error covariance update with accurate spacecraft mass property estimation results.

• ETRI Journal
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• v.39 no.1
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• pp.13-20
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• 2017

A method is proposed for scheduling sensor accesses to the shared network in a networked control system. The proposed method determines the access order in which the sensors are granted medium access through minimization of the state estimation error covariance. Solving the problem by evaluating the error covariance for each possible ordered set of sensors is not practical for large systems. Therefore, a convex optimization problem is proposed, which yields approximate yet acceptable results. A state estimator is designed for the augmented system resulting from the incorporation of the optimally chosen communication sequence in the plant dynamics. A car suspension system simulation is conducted to test the proposed method. The results show promising improvement in the state estimation performance by reducing the estimation error norm compared to round-robin scheduling.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.865-872
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• 2008

Spacecraft attitude estimation using the nonlinear unscented filter is addressed to fully utilize capabilities of the unscented transformation. To release significant computational load, an efficient technique is proposed by reasonably removing correlation between random variables. This modification introduces considerable reduction of sigma points and computational burden in matrix square-root calculation for most nonlinear systems. Unscented filter technique makes use of a set of sample points to predict mean and covariance. The general QUEST(QUaternion ESTimator) algorithm preserves explicitly the quaternion normalization, whereas extended Kalman filter(EKF) implicitly obeys the constraint. For spacecraft attitude estimation based on quaternion, an approach to computing quaternion means from sampled quaternions with guarantee of the quaternion norm constraint is introduced applying a constrained optimization technique. Finally, the performance of the new approach is demonstrated using a star tracker and rate-gyro measurements.

• Structural Engineering and Mechanics
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• v.22 no.4
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• pp.401-418
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• 2006

Solutions to the problems of structural parameter estimation from modal response using leastsquares minimization of force or displacement residuals are generally sensitive to noise in the response measurements. The sensitivity of the parameter estimates is governed by the physical characteristics of the structure and certain features of the noisy measurements. It has been shown that the regularization method can be used to reduce effects of the measurement noise on the estimation error through adding a regularization function to the parameter estimation objective function. In this paper, we adopt the regularization function as the Euclidean norm of the difference between the values of the currently estimated parameters and the a priori parameter estimates. The effect of the regularization function on the outcome of parameter estimation is determined by a regularization factor. Based on a singular value decomposition of the sensitivity matrix of the structural response, it is shown that the optimal regularization factor is obtained by using the maximum singular value of the sensitivity matrix. This selection exhibits the condition where the effect of the a priori estimates on the solutions to the parameter estimation problem is minimal. The performance of the proposed algorithm is investigated in comparison with certain algorithms selected from the literature by using a numerical example.

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.575-580
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• 2004

In this work, a method is presented to find an anatomical site of a current source crudely in a standard brain using grand average of MEG data. Minimum norm estimation algorithm and truncated singular value decomposition were applied to calculate the distributed sources that can reproduce the measured signals. Grand average over all subjects was obtained from the transformed signals, which would be detected in a standard sensor plane by the obtained distributed current sources. In the simulation study, it was shown that the localized dipole using the grand average is consistent with the mean location of localized dipoles of all subjects within several mm even with large inter-individual differences of sensor positions. This result suggests that the mean location of low level signal source can be estimated as a dipole source in grand average and it was confirmed in the localization of the current source of N100m. when the localized dipole is registered on a standard brain. This result also suggests that the activity region obtained from grand average can be crudely estimated on a standard brain using the source location of the N100m as a reference point.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1771-1776
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• 2003

In mobile robot navigation, one of the key problems is the pose estimation of the mobile robot. Although the odometry can be used to describe the motions of the mobile robots quite simple and accurately, the validities of the models are limited by a number of error sources contaminating the encoder outputs so that applying the conventional extended Kalman filter to these nominal model does not yield the satisfactory performance. As a remedy for this problem, we consider the uncertain nonlinear kinematic model of the mobile robot that contains the norm bounded uncertainties and also propose a new robust extended Kalman filter based on the Krein space approach. The proposed robust filter has the same recursive structure as the conventional extended Kalman filter and can hence be readily designed to effectively account for the uncertainties. The computer simulations will be given to verify the robustness against the parameter variation as well as the reliable performance of the proposed robust filter.