• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.213-218
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• 2021

It is known that total least-squares method shows better estimation performance than least-squares method when noise is present at the input and output at the same time. When total least squares method is applied to data with time series characteristics, Recursive Total Least Squares (RTS) algorithm has been proposed to improve the real-time performance. However, RTLS has numerical instability in calculating the inverse matrix. In this paper, we propose an algorithm for reducing numerical instability as well as having similar convergence to RTLS. For this algorithm, we propose a new RTLS using Iterative Wiener Filter (IWF). Through the simulation, it is shown that the convergence of the proposed algorithm is similar to that of the RTLS, and the numerical robustness is superior to the RTLS.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.399-406
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• 1996

This paper presents a method of hierarchical state estimation of the time-varying linear systems via Block-pulse function(BPF). When we estimate the state of the systems where noise is considered, it is very difficult to obtain the solutions because minimum error variance matrix having a form of matrix nonlinear differential equations is included in the filter gain calculation. Therefore, hierarchical approach is adapted to transpose matrix nonlinear differential equations to a sum of low order state space equation from and Block-pulse functions are used for solving each low order state space equation in the form of simple and recursive algebraic equation. We believe that presented methods are very attractive nd proper for state estimation of time-varying linear systems on account of its simplicity and computational convenience. (author). 13 refs., 10 figs.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.667-669
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• 1995

This paper presents a method to design Kalman filter on continuous stochastic dynamical systems via BPFT(block pulse functions transformation). When we design Kalman filter, minimum error valiance matrix is appeared as a form of nonlinear matrix differential equations. Such equations are very difficult to obtain the solutions. Therefore, in this paper, we simply obtain the solutions of nonlinear matrix differential equations from recursive algebraic equations using BPFT. We believe that the presented method is very attractive and proper for the evaluation of Kalman gain on continuous stochastic dynamical systems.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1694-1700
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• 2004

In this paper, we propose the AWLS/MFT (Adaptive Weighed Least Squares/ Modulation Function Technique) devised by A. E. Pearson et al. for the transfer function estimation of a modal system and investigate the performance of several algorithms, the Gram matrix method, a Luenberger Observer (LO), Least Squares (LS), and Recursive Least Squares (RLS), for the estimation of initial conditions. With the benefit of the Modulation Function Technique (MFT), we can separate the estimation problem into two phases: the transfer function parameters are estimated in the first phase, and the initial conditions are estimated in the second phase. The LO method produces excellent IC estimates in the noise free case, but the other three methods show better performance in the noisy case. Finally, we compared our result with the Prony based method. In the noisy case, the AWLS and one of the three methods - Gram matrix, LS, and RLS- show better performance in the output Signal to Error Ratio (SER) aspect than the Prony based method under the same simulation conditions.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.755-757
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• 1999

The operational properties of BPF(block-pulse functions) are much applied to the analysis of time-varying linear systems. The integral operational matrix of BPF converts the systems in the form of the differential equation into the algebraic problems. But the errors caused by using the integral operational matrix make it difficult that we exactly analyze time-varying linear systems. So, in this paper, to analyze time-varying linear systems we had used the recursive algorithm derived from the new integral operational matrix. And the usefulness of the proposed method is verified by the example.

• 전자공학회논문지 IE
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• v.48 no.1
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• pp.7-15
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• 2011

In this paper, we propose a correlation-based subspace estimation technique, which is called square-root forward/backward correlation-based projection approximation subspace tracking(SRFB-COPAST). The SRFB-COPAST utilizes the forward and backward correlation matrix as well as square-root recursive matrix update in projection approximation approach to develop the subspace tracking algorithm. With the projection approximation, the square-root recursive FB-COPAST is presented. The proposed algorithm has the better performance than the recently developed COPAST method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2731-2736
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• 2009

In this paper, a new implementation method of OFDM (orthogonal frequency division multiplexing) system with an orthogonal basis matrix is developed mathematically. The basis matrix is based on the Haar basis but has an appropriate form for modulation of multiple subchannel signals of OFDM. It is verified that the new basis matrix can be expanded with a simple recursive algorithm. The order of synthesis matrix in the transmitter is increased by the factor of two with every expansion. Demodulation in the receiver is carried out by its inverse matrix, which can be generated recursively with the orthogonal basis matrix. It is shown that perfect reconstruction of original signals is possibly achieved in the proposed OFDMsystem.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.32-37
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• 2020

This paper proposes a new l₁-norm-Recursive Least Squares (RLS) algorithm which is numerically more robust than the conventional l₁-norm-RLS. The l₁-norm-RLS was proposed by Eksioglu and Tanc in order to estimate the sparse acoustic channel. However the algorithm has numerical instability in the inverse matrix calculation. In this paper, we propose a new algorithm which is robust against the numerical instability. We show that the proposed method improves stability under several numerically erroneous situations.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1738-1739
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• 2011

In this paper, the range difference based the moving target tracking problem using multiple UAVs is solved within the new framework of linear robust state estimation. To do this, the relative kinematics is modeled as an uncertain linear system containing stochastic parametric uncertainties in its measurement matrix. Applying the non-conservative robust Kalman filter for the uncertain system, a quasi-optimal linear target tracking filter is designed. For its recursive linear filter structure, the proposed method can ensure the fast convergence and reliable target tracking performance. Moreover, it is suitable for real-time applications using multiple UAVs.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.12-25
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• 1994

A two dimensional constant geometry FFT algorithms and architectures with shuffled inputs and normally ordered outputs are presented. It is suitable for VLSI implementation because all buterfly stages have identical, regular structure. Also a methodology using shuffled FFT inputs and outputs to halve the number of butterfly stages connected by a global interconnection which requires much area is presented. These algorithms can be obtained by shuffling the row and column of a decomposed FFT matrix which corresponds to one butterfly stage. Using non-recursive and recursive pipeline, the degree of serialism and parallelism in FFT computation can be adjusted. To implement high performance high radix FFT easily and reduce the amount of interconnections between stages, the method to build a high radix PE with lower radix PE 's is discussed. Finally the performances of the present architectures are evaluated and compared.