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DESIGN OF ADAPTIVE FILTERS FOR A CLASS OF NONUNIFORMLY SAMPLED SIGNALS

SIGNAL PROCESSING 1993年第1期31卷 81-89页

作者： KRISHNA, VH REDDY, DC OSMANIA UNIV COLL ENGNDEPT ELECTR & COMMUN ENGNHYDERABAD 500007ANDHRA PRADESHINDIA

Conventional adaptive filters are designed on the assumption that signal sequences are uniformly spaced. However, there are several situations in real life when this is not the case. Although the design of adaptive filters for the general non-uniformly sampled signals is admittedly very difficult, nevertheless there exists a class of such signals for which it is possible to do so. The various issues involved in the design of such filters for the specified class are discussed in this paper. Performance results of the actually designed filters are also given.

关键词： ADAPTIVE FILTER UNIFORMLY SAMPLED SEQUENCE (USS) NONUNIFORMLY SAMPLED SEQUENCE (NUSS) lms algorithm

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Distributed estimation over complex adaptive networks with noisy links

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SMART STRUCTURES AND SYSTEMS 2017年第4期19卷 383-391页

作者： Farhid, Morteza Sedaaghi, Mohammad H. Shamsi, Mousa Sahand Univ Technol Dept Elect Engn Tabriz Iran

In this paper, we investigate the impacts of network topology on the performance of a distributed estimation algorithm, namely combine-then-adaptive (CTA) diffusion lms, based on the data with or without the assumptions of temporal and spatial independence with noisy links. The study covers different network models, including the regular, small-world, random and scale-free whose the performance is analyzed according to the mean stability, mean-square errors, communication cost (link density) and robustness. Simulation results show that the noisy links do not cause divergence in the networks. Also, among the networks, the scale free network (heterogeneous) has the best performance in the steady state of the mean square deviation (MSD) while the regular is the worst case. The robustness of the networks against the issues like node failure and noisier node conditions is discussed as well as providing some guidelines on the design of a network in real condition such that the qualities of estimations are optimized.

关键词： complex networks adaptive networks diffusion mode distributed estimation lms algorithm

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Performance analysis of adaptive Volterra filters in the finite-alphabet input case

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EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING 2004年第17期2004卷 2715-2722页

作者： Besbes, H Jaïdane, M Ezzine, J Ecole Super Commun Tunis Ariana 2083 Tunisia ENIT Tunis 1002 Tunisia

This paper deals with the analysis of adaptive Volterra filters, driven by the lms algorithm, in the finite-alphabet inputs case. A tailored approach for the input context is presented and used to analyze the behavior of this nonlinear adaptive filter. Complete and rigorous mean square analysis is provided without any constraining independence assumption. Exact transient and steady-state performances expressed in terms of critical step size, rate of transient decrease, optimal step size, excess mean square error in stationary mode, and tracking nonstationarities are deduced.

关键词： adaptive Volterra filters lms algorithm time-varying channels finite-alphabet inputs exact performance analysis

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TIME-DELAY ESTIMATION AND OPTIMAL-CONTROL CONCEPTS

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SIGNAL PROCESSING 1987年第3期12卷 291-307页

作者： ABUTALEB, AS Department of Electrical Engineering College of Engineering Computer Science and Architecture Temple University Philadelphia PA 19122 U.S.A.

In this paper, a nonlinear adaptive filter is introduced and applied to the classical problem of time delay estimation. The signal is modeled as the output of a linear time-varying filter driven by white noise. The filter structure is estimated using the Pontryagin minimum principle, in a novel way, which is then used to find an estimate for the delayed and undelayed signal and consequently the time delay is estimated. The resultant equations comprise a nonlinear filter (which is called a Pontryagin filter) that exhibits, on average, better performance than the lms based algorithm as shown through Monte Carlo simulation studies. In these studies, different SNR values and different time delays were simulated. The estimate of the time delay using both Pontryagin and lms algorithms are compared and it is shown that the nonlinear Pontryagin filter outperforms the commonly used lms algorithm and results in about 90% reduction in computer time. Zusammenfassung Eine wohlbekannte Methode zur Abschätzung von Zeitverzögerungen besteht darin, die Verzögerung mit Hilfe eines nichtrekursiven oder rekursiven Digitalfilters zu modellieren. Werden zwei Sensoren verwendet, so dient das empfangene Signal des einen Sensors als Eingangssignal des digitalen Filters, an dessen Ausgang das empfangene Signal des anderen Sensors möglichst gut nachgebildet werden soll. Der Schätzwert für die Zeitverzögerung wird aus den Koeffizienten des Filters berechnet, die beispielsweise mit Hilfe eines Kleinste-Quadrate-algorithmu geschätzt und aktualisiert werden. Dieses Verfahren hat einige bekannte Schwachstellen. (1) Die Schätzung der Zeitverzögerung mit Hilfe eines linearen zeitinvarianten Filters ist nur eine grobe Annäherung an die Realität. Ein lineares (oder nichtlineares) zeitveränderliches Filter wird sich bei geringerem Aufwand besser verhalten. (2) Um die Genauigkeit des Schätzwertes zu verbessern, mag es notwendig sein, die Zahl der Filterkoeffizienten drastisch zu erhöhen; dies bedeutet e

关键词： Time delay estimation lms algorithm invariant imbedding Pontryagin minimum principle

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Convolutive blind signal separation with post-processing

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IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING 2004年第5期12卷 539-548页

作者： Low, SY Nordholm, S Togneri, R Western Australian Telecommun Res Inst Perth WA 6009 Australia Univ Western Australia Sch Elect & Elect Engn Perth WA 6009 Australia

A new subband based speech enhancement scheme is presented. It integrates spatial and temporal signal processing methods to enhance speech signals in a noisy environment. The approach makes use of the popular blind signal separation (BSS) to spatially separate the target signal from the interference. Due to the multipath/reverberant environment, BSS has its fundamental limitation in its separation quality. To overcome that, an adaptive noise canceller (ANC) is employed to perform further interference reduction. The reference for the ANC in this case is simply the interference dominant output from the BSS. A higher order statistical method is proposed for the selection of the reference signal. This post processing acts as a spectral decorrelator and experimental results show that even in under-determined (more sources than elements) case, the structure offers impressive enhancement capability. Further, a remarkable improvement in recognition rate is registered when tested in automatic speech recognition (ASR).

关键词： adaptive noise canceller blind source separation kurtosis lms algorithm speech recognition subband

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Adaptive algorithm based on least mean p-power error criterion for Fourier analysis in additive noise

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1999年第4期47卷 1172-1181页

作者： Xiao, YG Tadokoro, Y Shida, K Saga Univ Fac Sci & Engn Saga 840 Japan Toyohashi Univ Technol Fac Engn Toyohashi Aichi Japan

This work presents a novel adaptive algorithm for the estimation of discrete Fourier coefficients (DFC) of sinusoidal and/or quasi-periodic signals in additive noise, The algorithm is derived using a least mean p-power error criterion. It reduces to the conventional lms algorithm when p, takes on 2, It is revealed by both analytical results and extensive simulations that the new algorithm for p = 3, 4 generates much improved DFC estimates in moderate and high SNR environments compared with the LIMS algorithm, whereas both have similar degrees of complexity. Assuming the Gaussian property of the estimation error, the proposed algorithm, including the lms algorithm, is analyzed in detail. Elegant dynamic equations and closed-form noise misadjustment expressions are derived and clarified.

关键词： adaptive Fourier analysis least mean p-power error criterion lms algorithm noise misadjustment

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A New Mixed Variable Step Size Elms algorithm and Its Application in ANC

A New Mixed Variable Step Size ELMS Algorithm and Its Applic...

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The 2011 International Conference on Computing,Information and Control(ICCIC 2011)

作者： Sun Li Jun Yong Li School of Electrical Engineering Henan University of Technology School of Electrical Engineering Henan University of TechnologyZhang Shou School of Electrical Engineering Henan University of TechnologyWang Xiang

In this paper, we proposed a new mixed variable step size Elms algorithm(MVSS-Elms) which combined the mean square error and the correlation of the error to modify the step size. The approach is general in the sense that it not only retains the benefits of Elms algorithm lower steady-state error, but also improves the system convergence speed. In comparison, the new approach performance is much better than VSS-Elms algorithm and VFSSElms algorithm in the convergence speed and anti-noise capacity. Effectiveness of the proposed algorithm is demonstrated through computer simulations.

关键词： adaptive filtering noise canceller lms algorithm Elms algorithm

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Dual-antenna RF CMOS front-end for interferer removal in ultra-wideband systems

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2008年第3期27卷 381-390页

作者： Neumann, Dirk K. Hoffman, MichaelW. Balkir, Sina Syst Engn & Patents Ubidyne GmbH D-89081 Ulm Germany Univ Nebraska Dept Elect Engn Walter Scott Engn Ctr 209N Lincoln NE 68588 USA

Many ultra-wideband (UWB) systems are challenged by strong jammers and narrowband interferers. Using two antennas, we demonstrate a robust UWB radio frequency (RF) front-end design in a 0.25 mu m mixed-signal complementary metal oxide semiconductor (CMOS) technology. The proposed realization is capable of adaptively removing a high-power, narrowband interferer early in the receiver chain avoiding front-end saturation and preserving UWB signal power. The early interferer removal resulting in interferer-free demodulation is based on the least mean squares (lms) algorithm and achieved through a novel combiner low-noise amplifier and noise optimized filtering. Circuit level RF simulations of the proposed circuitry indicate a maximum improvement in signal-to-interference ratio of 39.6 dB.

关键词： UWB interferer removal interferer rejection RF CMOS front-end lms algorithm

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The q-Least Mean Squares algorithm

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SIGNAL PROCESSING 2015年 111卷 50-60页

作者： Al-Saggaf, Ubaid M. Moinuddin, Muhammad Arif, Muhammad Zerguine, Azzedine King Abdulaziz Univ Dept Elect & Comp Engn Jeddah Saudi Arabia King Abdulaziz Univ CEIES Jeddah Saudi Arabia PAP Karachi Inst Econ & Technol Univ Dept Elect Engn Karachi Pakistan King Fand Univ Petr & Minerals Dept Elect Engn Dhahran Saudi Arabia

The Least Mean Square (lms) algorithm inherits slow convergence due to its dependency on the eigenvalue spread of the input correlation matrix. In this work, we resolve this problem by developing a novel variant of the lms algorithms based on the q-derivative concept. The q-gradient is an extension of the classical gradient vector based on the concept of Jackson's derivative. Here, we propose to minimize the lms cost function by employing the concept of q-derivative instead of the convent ional derivative. Thanks to the fact that the q-derivative takes larger steps in the search direction as it evaluates the secant of the cost function rather than the tangent (as in the case of a conventional derivative), we show that the q-derivative gives faster convergence for q >1 when compared to the conventional derivative. Then, we present a thorough investigation of the convergence behavior of the proposed q-lms algorithm and carry out different analyses to assess its performance. Consequently, new explicit closed-form expressions for the mean-square-error (MSE) behavior are derived. Simulation results are presented to corroborate our theoretical findings. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Adaptive filters q-gradient q-lms algorithm lms algorithm Transient analysis Steady-state analysis

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THE RESEARCH FOR SPEECH SIGNAL ENHANCEMENT BASED ON Mlms algorithm

THE RESEARCH FOR SPEECH SIGNAL ENHANCEMENT BASED ON MLMS ALG...

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2007中国宁波国际计算机科学与技术学术大会

作者： Xu Yan Wu Xiaosuo He Tianbing School of Electronic and Information Engineering Lanzhou Jiaotong University

Lest Mean Square(lms) is adaptive noise cancellation method to extract weak voice signals from strong background *** to the low convergence rate and narrow frequency band extracting signal of lms,an improved Momentum Lest Mean Square(Mlms) algorithm is given in the paper by analyzing the performance of lms *** results of simulations indicate that adaptive filter technology for solving weak signal extracting problem is effective.

关键词： lms algorithm Speech enhancement Noise cancellation Adaptive filter

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