• The Journal of the Acoustical Society of Korea
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• v.29 no.6
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• pp.374-380
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• 2010

It is known that the problem of FIR filtering with noisy input and output data can be solved by a total least squares (TLS) estimation. It is also known that the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose a convex combination algorithm between the ordinary recursive LS based TLS (RTLS) and the ordinary recursive LS (RLS). This combined algorithm is robust to the noise variance ratio and has almost the same complexity as the RTLS. Simulation results show that the proposed algorithm performs near TLS in noise variance ratio ${\gamma}{\approx}1$ and that it outperforms TLS and LS in the rage of 2 < $\gamma$ < 20. Consequently, the practical workability of the TLS method applied to noisy data has been significantly broadened.

• The Journal of the Acoustical Society of Korea
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• v.22 no.3E
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• pp.93-100
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• 2003

We present a recursive total least squares (RTLS) algorithm for adaptive system identification. So far, recursive least squares (RLS) has been successfully applied in solving adaptive system identification problem. But, when input data contain additive noise, the results from RLS could be biased. Such biased results can be avoided by using the recursive total least squares (RTLS) algorithm. The RTLS algorithm described in this paper gives better performance than RLS algorithm over a wide range of SNRs and involves approximately the same computational complexity of O(N²).

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2344-2353
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• 2018

A coupled recursive total least squares (CRTLS) algorithm is proposed for parameter estimation of permanent magnet synchronous machines (PMSMs). TLS considers the errors of both input variables and output ones, and thus achieves more accurate estimates than standard least squares method does. The proposed algorithm consists of two recursive total least squares (RTLS) algorithms for the d-axis subsystem and q-axis subsystem respectively. The incremental singular value decomposition (SVD) for the RTLS obtained by an approximate calculation with less computation. The performance of the CRTLS is demonstrated by simulation and experimental results.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.527-530
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• 2004

We present a recursive total least squares (RTLS) algorithm for multilayer feedforward neural networks. So far, recursive least squares (RLS) has been successfully applied to training multilayer feedforward neural networks. But, when input data contain additive noise, the results from RLS could be biased. Such biased results can be avoided by using the recursive total least squares (RTLS) algorithm. The RTLS algorithm described in this paper gives better performance than RLS algorithm over a wide range of SNRs and involves approximately the same computational complexity of $O(N^{2})$.

• 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.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.23-27
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• 1991

RLS(Recursive Least Squares)나 LMS(Least mean square)등은 알고리듬 고유의 성질상 잡음이 섞인 시스템에 있어서는 올바른 역 모델링을 할 수 없다. 따라서, 잡음의 영향을 받지않는 견실한(robust) 모델 추정 알고리듬이 필요하다. 본 논문에서는 잡음환경하에 있는 시스템을역 모델링하는데 있어서, 잡음의 영향을 줄이기위해 완전최소자승법을 도입하고 기존의 최소자승법과 비교 실험하였다. 그리고, 이 방법의 적응 알고리듬을 제안하였으며, RLS(Recursive least squares)와 그 성능을 비교하여 타당성을 검토하였다.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.547-550
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• 2004

본 논문은 시스템 인식에서 RLS의 성능을 높이기 위한 한 방법으로 UDU 행렬 분해법을 바탕으로 한 recursive total least squares (RTLS) algorithm을 제안한다. 기존의 RTLS는 Power Method에 의거해서 recursive하게 만든 형태이어서 RLS와 거의 같은 구조이다. 그러나 본 논문에서는 일반적인 Power Method가 rank-1 update를 이용하기 때문에 ill-condition에 빠질 가능성이 높은 점을 감안하여, UDU 행렬 분해법을 사용한 RTLS방법을 제안하고, 그를 시스템 인식에 적용한다.

• Journal of Electrical Engineering and Technology
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• v.7 no.4
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• pp.606-614
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• 2012

A practical recursive linear robust estimation scheme is proposed for target localization in the sensor network which provides range difference of arrival (RDOA) measurements. In order to radically solve the known practical difficulties such as sensitivity for initial guess and heavy computational burden caused by intrinsic nonlinearity of the RDOA based target localization problem, an uncertain linear measurement model is newly derived. In the suggested problem setting, the target localization performance of the conventional linear estimation schemes might be severely degraded under the low SNR condition and be affected by the target position in the sensor network. This motivates us to devise a new sensor network localization algorithm within the framework of the recently developed robust least squares estimation theory. Provided that the statistical information regarding RDOA measurements are available, the estimate of the proposition method shows the convergence in probability to the true target position. Through the computer simulations, the omnidirectional target localization performance and consistency of the proposed algorithm are compared to those of the existing ones. It is shown that the proposed method is more reliable than the total least squares method and the linear correction least squares method.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.357-360
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• 2002

혈관에 흐르는 혈류 속도의 측정은 혈압 및 심박수와 관련된 혈류의 역학적 변화를 관찰하는 데 있어서 주로 사용되는 방법 중의 하나이다. 이 혈류 속도는 일반적으로 도플러 효과에 의하여 주파수가 변화하는 현상을 이용하여 추정하게 된다. 그런데 기존의 주파수 추정 방법들은 시불변 시스템을 가정하고 있지만 실제 혈관 속은 혈구가 일정하지 않은 속도를 갖는 시변 시스템이라 할 수 있기 때문에 이러한 시변 특성이 강한 경우 기존의 방법을 이용하게 되면 그 성능이 저하되는 경향을 보인다. 또 피시험자의 몸 상태에 따라서 서로 다른 주파수 변화 추이를 보이므로 하나의 고정 변수로써 최적화된 성능을 기대하기도 어렵다. 그러므로 본 논문에서는 시변 시스템에서 좋은 성능을 갖는 가변 망각 인자(variable forgetting factor, VFF)를 사용한 순환적인 완전 최소 자승법(recursive total least squares, RTLS) 기법을 이용한 주파수 추정 방법을 제안한다. RTLS란 TLS 기법을 순차적으로 계산하는 방법으로 시변 적응력을 향상시키는 방법이다. 또한 이 기법에 가변 망각 인자(VFF)를 적용시키는 것은 시변 시스템에서 외부적인 변화에 대하여 좀더 효율적으로 대응할 수 있기 위함이다. 기존의 방법과 성능 비교를 위하여 컴퓨터 시뮬레이션을 하였으며 그 결과 시변 시스템에서 본 논문에서 제안한 VFF를 이 용한 RTLS 기법이 보다 향상된 성능을 가지고 있음을 확인 할 수 있었다.

• The Journal of the Acoustical Society of Korea
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• v.14 no.3
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• pp.37-48
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• 1995

This paper presents an sstimation of ARMA coefficients of noisy ARMA system using generalized total least square (GTLS) method. GTLS problem for ARMA system is defined as minimizing the errors between the noisy output vectors and estimated noisy-free output. The GTLS problem is solved in closed form by eigen-problem and the perturbation analysis of GTLS is presented. Also its recursive solution (recursive GTLS) is proposed using the power method and the covariance formula of the projected output error vector into the input vector space. The simulation results show that GTLS ARMA coefficients estimator is an unbiased estimator and that recursive GTLS achieves fast convergence.