• Journal of Communications and Networks
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• 제18권1호
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• pp.8-18
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• 2016

In this paper, we propose a number of blind equalization approaches for time-varying andmulti-path channels. The approaches employ cost reference particle filter (CRPF) as the symbol estimator, and additionally employ either least mean squares algorithm, recursive least squares algorithm, or $H{\infty}$ filter (HF) as a channel estimator such that they are jointly employed for the strategy of "Rao-Blackwellization," or equally called "mixture filtering." The novel feature of the proposed approaches is that the blind equalization is performed based on direct channel estimation with unknown noise statistics of the received signals and channel state system while the channel is not directly estimated in the conventional method, and the noise information if known in similar Kalman mixture filtering approach. Simulation results show that the proposed approaches estimate the transmitted symbols and time-varying channel very effectively, and outperform the previously proposed approach which requires the noise information in its application.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.655-658
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• 1999

In this study, We derived Recursive Least Squares(RLS) algorithm with adaptive maximum -likelihood channel estimate for digital pulse amplitude modulated sequence in the presence of intersymbol interference and additive white Gaussian noise. RLS algorithms have better convergence characteristics than conventional algorithms, LMS Least Mean Squares) algorithms.

• 한국음향학회지
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• 제38권3호
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• pp.283-289
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• 2019

RLS(Recursive-least-squares) 알고리즘은 수렴성이 좋고, 수렴 후 오차 수준도 우수한 것으로 알려져 있다. 그러나 알고리즘 내에 역행렬 계산이 포함되어 수치적 불안정성을 나타내는 단점도 있다. 본 논문에서는 언급한 불안정성을 회피하기 위해서 역행렬이 없지만 수렴성이 유사한 알고리즘을 제안한다. 이를 위해서 기울기 평균 벡터를 사용한 가변 스텝 최소 자승 알고리즘을 사용한다. 또 시변 채널 추정에 우수한 성능을 내기 위해서 계산량이 적은 가변 망각인자를 도입한다. 시뮬레이션을 통해서 기존 RLS와의 성능을 비교하고 그 유사성을 보인다. 또 시변 채널에서 가변 망각인자의 우수성도 보인다.

• 제어로봇시스템학회논문지
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• 제22권8호
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• pp.650-655
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• 2016

In this paper, a novel RLS-based DOB (Recursive Least Squares Disturbance Observer) scheme is proposed to improve the performance of DOB for nominal model identification. A nominal model can be generally assumed to be a second order system in the form of a proper transfer function of an ARMA (Autoregressive Moving Average) model. The RLS algorithm for the model identification is proposed in association with DOB. Experimental studies of the balancing control of a one-wheel robot are conducted to demonstrate the feasibility of the proposed method. The performances between the conventional DOB scheme and the proposed scheme are compared.

• 한국음향학회지
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• 제39권1호
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• pp.32-37
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• 2020

본 논문은 l₁놈-Recursive Least Squares(RLS)에 수치 계산상 견실화를 더한 새로운 알고리즘을 제안한다. Eksioglu와 Tanc는 희소성 음향 채널 추정을 위해서 l₁놈-RLS 알고리즘을 구현하였다. 그러나 이 알고리즘의 근간인 RLS 계산법 역행렬 계산에서 수치 계산상의 불안정성을 지니고 있다. 본 논문에서는 이런 불안정성을 낮추는 새로운 알고리즘을 제안한다. 그리고 제안한 방법을 사용했을 때 수치적 불안정성에 대한 성능이 개선되었음을 보인다.

• 대한기계학회논문집
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• 제12권4호
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• pp.694-704
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• 1988

본 연구에서는 현장에서 흔히 수행되는 가공 후(post-process) 측정만을 이용 한 단일절삭(single-pass)으로써 공작물이 원하는 일정한 직경을 유지할 수 있다는 것 을 실험을 통해 검증해 보이고자 한다. 이와 같은 방식은 이미 보오링 작업 중 보오 링바의 노출 길이 변화에 따른 가공면의 내경 오차를 없애주기 위한 방법으로써 제시 된 바 있다. 즉 반복 최소 자승법(recursive least squares)으로 미리 가정된 절삭 조건과 공작물 처짐 사이의 관계식과 관련된 계수를 가공 후에 가공 공작물의 표면 측 정으로 매 번보정(update)시켜 줌으로써 일정 직경을 얻기위한 절삭조건을 결정하고자 한다.

• 대한의용생체공학회:의공학회지
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• 제26권2호
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• pp.87-93
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• 2005

A new method for real-time estimating left ventricular relaxation time constant (T) from the left ventricular (LV) pressure waveform, based on the isovolumic relaxation model, is proposed. The presented method uses a recursive least squares (RLS) algorithm to accomplish real-time estimation. A new criterion to detect the end-point of the isovolumic relaxation period (IRP) for the estimation of T is also introduced, which is based on the pattern analysis of mean square errors between the original and reconstructed pressure waveforms. We have verified the performance of the new method in over 4,600 beats obtained from 70 patients. The results demonstrate that the proposed method provides more stable and reliable estimation of τ than the conventional 'off-line' methods.

• Journal of Electrical Engineering and Technology
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• 제7권4호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권8호
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• pp.3295-3311
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• 2020

With the wide application of hyperspectral images, it becomes more and more important to compress hyperspectral images. Conventional recursive least squares (CRLS) algorithm has great potentiality in lossless compression for hyperspectral images. The prediction accuracy of CRLS is closely related to the correlations between the reference bands and the current band, and the similarity between pixels in prediction context. According to this characteristic, we present an improved CRLS with adaptive band selection and adaptive predictor selection (CRLS-ABS-APS). Firstly, a spectral vector correlation coefficient-based k-means clustering algorithm is employed to generate clustering map. Afterwards, an adaptive band selection strategy based on inter-spectral correlation coefficient is adopted to select the reference bands for each band. Then, an adaptive predictor selection strategy based on clustering map is adopted to select the optimal CRLS predictor for each pixel. In addition, a double snake scan mode is used to further improve the similarity of prediction context, and a recursive average estimation method is used to accelerate the local average calculation. Finally, the prediction residuals are entropy encoded by arithmetic encoder. Experiments on the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) 2006 data set show that the CRLS-ABS-APS achieves average bit rates of 3.28 bpp, 5.55 bpp and 2.39 bpp on the three subsets, respectively. The results indicate that the CRLS-ABS-APS effectively improves the compression effect with lower computation complexity, and outperforms to the current state-of-the-art methods.

• 제어로봇시스템학회논문지
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• 제20권8호
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• pp.849-853
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• 2014

In micro-scale electromechanical systems, the power to perform accurate position sensing often greatly exceeds the power needed to generate motion. This paper explores the implications of sampling rate and amplifier noise density selection on the performance of a system identification algorithm using a capacitive sensing circuit. Specific performance objectives are to minimize or limit convergence rate and power consumption to identify the dynamics of a rotary micro-stage. A rearrangement of the conventional recursive least-squares identification algorithm is performed to make operating cost an explicit function of sensor design parameters. It is observed that there is a strong dependence of convergence rate and error on the sampling rate, while energy dependence is driven by error that may be tolerated in the final identified parameters.