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Performance analysis of a simplified rls algorithm for the estimation of sinusoidal signals in additive noise

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 1998年第8期E81A卷 1703-1712页

作者： Xiao, YG Tadokoro, Y Shida, K Iwamoto, K Saga Univ Fac Sci & Engn Saga 8408502 Japan Toyohashi Univ Technol Fac Engn Toyohashi Aichi 4418580 Japan TVQ Hakata Ku Fukuoka 4418580 Japan

Adaptive estimation of nonstationary sinusoidal signals or quasi-periodic signals in additive noise is of essential importance in many diverse engineering fields, such as communications, biomedical engineering, power systems, pitch detection in transcription and so forth. So far, Kalman filtering based techniques, recursive least square (rls), simplified rls (Srls) and LMS algorithms, for examples, have been developed for this purpose. This work presents in detail a performance analysis for the Srls algorithm proposed recently in the literature;which is used to estimate an enhanced sinusoid. Its dynamic and tracking properties, noise and lag misadjustments are developed and discussed. It is found that the Srls estimator is biased, and its misadjustments are functions of not only the noise variance but also, unpleasantly, of the signal parameters. Simulations demonstrate the validity of the analysis. Application of the Srls to a real-life piano sound is also given to peek at its effectiveness.

关键词： rls algorithm performance analysis tracking property misadjustment

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On steady-state performance of the fixed-point rls algorithm

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COMPUTERS & ELECTRICAL ENGINEERING 1999年第1期25卷 1-16页

作者： Adali, T Ardalan, SH Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA XCAD Corp Cary NC 27511 USA

This paper presents an analysis of a fixed-point recursive least squares (rls) algorithm for first-order Markov channel estimation and derives expressions for the mean weight misadjustment. The expressions derived are general in that they take into account the correlation in the input. It is shown that correlation amplifies the effect of roundoff error due to the desired signal estimate computation and the additive system noise. The misadjustment due to time-varying system weights and the weight update roundoff error behave similarly and are minimally affected by the input correlation. They contribute to the total misadjustment in such away that is directly proportional to the algorithm's time constant which is a function of the algorithm forgetting factor;The contributions of system noise and roundoff error due to the desired estimate, on the other hand, are inversely proportional to the algorithm time constant. Hence, they indicate a tradeoff in the choice of the forgetting factor to balance the effects of these noise sources. We present simulation results which demonstrate very good agreement with the theory, (C) 1999 Elsevier Science Ltd, All rights reserved.

关键词： fixed-point analysis rls algorithm system identification roundoff errors time-varying systems steady-state performance recursive least squares algorithm input correlation effects

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Theoretical Analysis of the Performance of the Data-Reuse rls algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第1期71卷 490-494页

作者： Gao, Wei Chen, Jie Richard, Cedric Jiangsu Univ Sch Comp Sci & Telecommun Engn Zhenjiang 212013 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Univ Cote Azur CNRS OCA F-06108 Nice France

The data-reuse rls (DR-rls) algorithm is a computationally efficient technique that has been recently introduced to improve the tracking capabilities of the rls. Nevertheless, no analysis of its convergence has been proposed so far. The aim of that work is to fill this gap with theoretical analyzes in the sense of the mean and mean-square error. Both transient and steady-state analytical models are provided. Their effectiveness and accuracy are validated through numerical simulations.

关键词： Adaptive filtering data-reuse rls algorithm transient convergence analysis steady-state analysis

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Nonlinear rls algorithm for amplitude estimation in class A noise

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IEE PROCEEDINGS-COMMUNICATIONS 2000年第2期147卷 81-86页

作者： Weng, JF Leung, SH City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

An adaptive nonlinear recursive least square (rls) algorithm for amplitude estimation in class A noise is presented. For Gaussian input signal and class A noise, its mean and mean-square behaviours are studied. It is shown that the linear rls and nonlinear rls algorithm with the clipper function are stable in the mean and mean square. For non-Gaussian input, amplitude estimation in CDMA communication is presented. Simulation results show that the nonlinear rls can provide good performance close to the Cramer-Rao bound and outperform the nonlinear LMS and the conventional rls in impulse noise.

关键词： adaptive estimation Multiple access communication recursive estimation adaptive nonlinear recursive least square algorithm class A noise non-Gaussian input signal impulse noise CDMA communication nonlinear estimation Filtering methods in signal processing Interpolation and function approximation (numerical analysis) mean behaviour Gaussian input signal Cramer-Rao bound code division multiple access filtering theory convergence of numerical methods least squares approximations rls algorithm simulation results mean-square behaviour amplitude estimation clipper function

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Gradient based variable forgetting factor rls algorithm

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SIGNAL PROCESSING 2003年第6期83卷 1163-1175页

作者： So, CF Ng, SC Leung, SH Hong Kong Polytech Univ Dept Elect Engn Kowloon Hong Kong Peoples R China Open Univ Hong Kong Sch Sci & Technol Hong Kong Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China

An accurate new variable forgetting factor recursive least-square adaptive algorithm is derived. An improved mean square behaviour analysis is presented, which shows that the theoretical analysis and the simulation results are close to each other. The control of the forgetting factor is based on the dynamic equation of the gradient of mean square error. Compared with other variable forgetting factor algorithms, the new algorithm provides fast tracking and small mean square model error, and its performance will not be degraded much even in low signal-to-noise ratios. (C) 2003 Elsevier Science B.V. All rights reserved.

关键词： adaptive filters variable forgetting factor rls algorithm

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rls algorithm based sub-band decomposition for low memory multimedia applications

RLS algorithm based sub-band decomposition for low memory mu...

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8th International Conference on Advanced Communication Technology

作者： Patnaik, S Patnaik, Y Upadhya, A Natl Inst Technol Rourkela Dept Engn Rourkela India

ISBN: (纸本)8955191294

In this paper a neural-network based recursive algorithm for computation of the principal component of a stationary vector stochastic process is discussed. The optimal criteria is to retain the maximum information contained of the input, so as to be able to reconstruct the network inputs from the corresponding outputs with minimum mean square error. In this paper a multi-stage principal component extraction (PCE) algorithm is proposed for sub-band decomposition. In multimedia applications though wavelet or sub-band coding of image has been shown to be superior to more traditional transform coding techniques, little attention has been paid to the important issue of whether both the wavelet transforms and the subsequent can be implemented in low memory without significant loss in performance. This paper addresses the problem of low memory image compression and proposes a block based, reduced memory PCE based, image compression algorithm suitable for multimedia online applications.

关键词： rls algorithm sub-band coding image compression

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ANALYSIS OF OVERSHOOT PHENOMENA IN INITIALIZATION STAGE OF rls algorithm

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SIGNAL PROCESSING 1995年第3期44卷 329-339页

作者： EOM, KS PARK, DJ KOREA ADV INST SCI & TECHNOL DEPT ELECT ENGNTAEJON 305701SOUTH KOREA

The well-known recursive least-squares (rls) algorithm experiences overshoot in the norm of the parameter error vector temporarily during the initialisation stage when the initial value of the input autocorrelation matrix is chosen very small. We have had no guide to choose a suitable initial value of the input autocorrelation matrix in order to cause the rls algorithm not to fluctuate during the initialisation phase. In this paper, deriving the bound of parameter error norm square, we give a useful guide to choose the initial value in order to avoid the initial overshoot in the rls algorithm. Besides, we show that what are the crucial factors of the overshoot phenomena. The ratio of the initial value to the input power should be chosen according to the signal-to-noise ratio of the system and the order of the filter. We present various simulation results to validate our analysis.

关键词： ADAPTIVE FILTERING INITIAL OVERSHOOT rls algorithm

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Adaptive recovery of a chirped signal using the rls algorithm

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1997年第2期45卷 363-376页

作者： Wei, PC Zeidler, JR Ku, WH USN COMMAND CONTROL & OCEAN SURVEILLANCE CTRRDT&E DIVSAN DIEGOCA 92125

This paper studies the performance of the recursive least squares (rls) algorithm in the presence of a general chirped signal and additive white noise, The chirped signal, which is a moving average (MA) signal deterministically shifted in frequency at rate psi, can be used to model a frequency shift in a received signal, General expressions for the optimum Wiener-Hopf coefficients, one-step recovery and estimation errors, noise and lag misadjustments, and the optimum adaptation constant (beta(opt)) are found in terms of the parameters of the stationary MA signal, The output misadjustment is shown to be composed of a noise (xi(0)M beta/2) and lag term (kappa/(beta(2) psi(2))), and the optimum adaptation constant is proportional to the chirp rate as psi(2/3). The special case of a chirped first-order autoregressive (ARI) process with correlation (alpha) is used to illustrate the effect the bandwidth (1/alpha) of the chirped signal on the adaptation parameters, It is shown that unlike for the chirped tone, where the beta(opt) increases with the filter length (M), the adaptation constant reaches a maximum for M near the inverse of the signal correlation (1/alpha). Furthermore, there is an optimum filler length for tracking the chirped signal and this length is less than (1/alpha).

关键词： .white noise rls algorithm filter length optimum filter Adaptive Recovery Signal Using noise and lag misadjustments optimum adaptation

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Variable Forgetting Factor rls algorithm-Based Channel Estimator for MC-IDMA Communication Systems

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WIRELESS PERSONAL COMMUNICATIONS 2015年第2期83卷 1373-1386页

作者： Oyerinde, Olutayo Oyeyemi Mneney, Stanley Henry Univ Witwatersrand Sch Elect & Informat Engn ZA-2050 Johannesburg South Africa Univ KwaZulu Natal Sch Engn Elect Elect & Comp Engn ZA-4041 Durban South Africa

There are different types of technologies that are in use for multi-user wireless communication systems. In this article, a relatively new multi-user scheme, Multi-Carrier Interleave Division Multiple Access (MC-IDMA) is considered. The scheme is an attractive multiuser technique that has generated a large interest in the research community. In this article the channel estimation area of the wireless communication system is investigated. A variable forgetting factor-recursive least mean square (VFF-rls) algorithm is proposed for channel impulse response (CIR) estimation in the MC-IDMA systems. The performance of the proposed adaptive VFF-rls based-time domain iterative channel estimator is validated in comparison with the LMS-based channel estimator and another channel estimation scheme employing both channel transfer function based on sequential linear minimum mean square error estimator and the adaptive prediction of the time domain CIR earlier proposed in literature. The simulation results indicate that the proposed VFF-rls based-time domain iterative channel estimator outperforms its counterparts and it is a good compromise in terms of performance and computational complexity.

关键词： Adaptive algorithms Channel estimation MC-IDMA Multi-user wireless communication system rls algorithm

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Adaptive filtering in cascade form using a fast multichannel rls algorithm

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL SIGNAL PROCESSING 1997年第9期44卷 757-762页

作者： Coradine, LC Romano, JMT Bellanger, MG UNIV FED ESPIRITO SANTO DELCTVITORIAESBRAZIL CONSERVATOIRE NATL ARTS & METIERS LAB ELECT & COMMUNPARIS 03FRANCE

This brief proposes an approach to apply least-squares techniques to adaptive FIR filtering in cascade form. A triangular structure is combined with an adaptive multichannel filter, which can exploit a fast recursive least squares (rls) algorithm. The resulting approximation and the behavior of the system in the learning case is discussed. Simulations results are given for the linear prediction case, for which an additional adaptive procedure is derived. Sinusoidal signals are used at the input. since the inherent cascade structure directly yields the frequencies of the sinusoids from the section coefficients. The proposed method brings the efficiency of least-squares techniques, in terms of accuracy and convergence rate, to adaptive filter in cascade form.

关键词： adaptive filtering cascade structure linear prediction rls algorithm

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