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Bézier Curves for Smooth Entry into Elliptic Orbits
Vladimir Shin,Mirzobek Malikov,김윤수 한국항공우주학회 2024 International Journal of Aeronautical and Space Sc Vol.25 No.2
This paper describes a path planning approach for smooth entry of an aerospace vehicle (ASV) into a 3D elliptic orbit. The generated path represents a polynomial Bézier curve connecting a given position of the ASV with the orbital entry point. Recursive and non-recursive analytical formulas for the last k intermediate control points determining Ck smooth Bézier path are derived. To evaluate the performance of the proposed approach, a practical measure of path smoothness such as path length, average curvature, and maximum curvature is introduced to choose the best entry point on the orbit and the corresponding Bézier path. The simulation results demonstrate that minimizing the maximum curvature yields a path that optimizes the proposed smoothness measure.
Shin, Vladimir,Thien, Rebbecca T. Y.,Kim, Yoonsoo Hindawi Limited 2018 Mathematical problems in engineering Vol.2018 No.-
<P>This paper presents a noise covariance estimation method for dynamical models with rectangular noise gain matrices. A novel receding horizon least squares criterion to achieve high estimation accuracy and stability under environmental uncertainties and experimental errors is proposed. The solution to the optimization problem for the proposed criterion gives equations for a novel covariance estimator. The estimator uses a set of recent information with appropriately chosen horizon conditions. Of special interest is a constant rectangular noise gain matrices for which the key theoretical results are obtained. They include derivation of a recursive form for the receding horizon covariance estimator and iteration procedure for selection of the best horizon length. Efficiency of the covariance estimator is demonstrated through its implementation and performance on dynamical systems with an arbitrary number of process and measurement noises.</P>
New Algorithm for Recursive Estimation in Linear Discrete-Time Systems with Unknown Parameters
Vladimir Shin,Jun Il Ahn,Du Yong Kim 대한전기학회 2006 International Journal of Control, Automation, and Vol.4 No.4
The problem of recursive filtering for linear discrete-time systems with uncertainties is considered. A new suboptimal filtering algorithm is herein proposed. It is based on the fusion formula, which represents an optimal mean-square linear combination of local Kalman estimates with weights depending on cross-covariances between local filtering errors. In contrast to the optimal weights, the suboptimal weights do not depend on current measurements, and thus the proposed algorithm can easily be implemented in real-time. High accuracy and efficiency of the suboptimal filtering algorithm are demonstrated on the following examples: damper harmonic oscillator motion and vehicle motion constrained to a plane.
Layouts and Cells in Integral Photography and Point Light Source Model
Vladimir V. Saveljev,Seung-Jung Shin 한국광학회 2009 Current Optics and Photonics Vol.13 No.1
The similarity between two groups of displaying methods is demonstrated in two ways, analytically and experimentally. A variety of layouts of the integral photography and display devices based on the point light source model is classified and analyzed in terms of projections and common/ separate image planes. In particularly, the transformation matrix is found. Simulation experiments based on the image processing were performed. The layouts, analytical formulas, and experimental results show the similarity of both groups for several layouts.
Effective Computation Algorithms for Fusion Estimation
Seokhyoung Lee,Vladimir Shin 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper multisensory distributed fusion estimation algorithms are considered. We state new formulaswhich address the computation of matrix weights arising from multidimensional fusion estimation problems. This paperprovides two computationally effective algorithms for computation of matrix weights. The first algorithm is based on Cholesky factorization of a cross covariance block matrix. This algorithm has low computational complexity and it is equivalent to the standard composite fusion estimation algorithm as well. The second algorithm is based on special approximation scheme for local cross-covariances. Such approximation is useful to compute matrix weights for fusionestimation in multidimensional-multisensor environment. Subsequent computational analysis of the proposed fusion algorithms is presented with a corresponding example showing the low computational complexities of the new fusionestimation algorithms.
Song, Il Young,Shin, Vladimir IOP Pub 2010 Measurement Science and Technology Vol.21 No.12
<P>A new distributed receding horizon filtering algorithm for mixed continuous–discrete linear systems with different types of observations is proposed. The distributed fusion filter is formed by summation of the local receding horizon Kalman filters (LRHKFs) with matrix weights depending only on time instants. The proposed distributed filter has a parallel structure and allows parallel processing of measurements; thereby, it is more reliable than the centralized version if some sensors become faulty. Also, the selection of the receding horizon strategy makes the proposed distributed filter robust against dynamic model uncertainties. The key contribution of this paper is the derivation of the error cross-covariance equations between the LRHKFs in order to compute the optimal matrix weights. High accuracy and efficiency of the proposed distributed filter are demonstrated on the damper harmonic oscillator motion and the water tank mixing system.</P>