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A Cyclic Frequency Tracking Loop for Wideband Spectrum Sensing and FM Demodulation

IEEE COMMUNICATIONS LETTERS 2016年第10期20卷 2011-2014页

作者： Garakyaragh, Nooshin Shahtalebi, Kamal Forouzan, Amir R. Univ Isfahan Dept Elect Engn Esfahan *** Iran

A cyclostationary feature-based wideband spectrum sensing is proposed in which cyclic frequency offset is considered. The received signal passes through a rough and flexible filter which its effective band tuned to a specific part of the received signal spectrum. It belongs to a target signal, which possibly exists in the received signal. Some cyclic frequencies of the target are employed to derive the normalized least mean square algorithm that estimates the filter output from its frequency shifted samples. Based on the weight estimate's norm, the algorithm is tuned to find the accurate cyclic frequencies. If it succeeded, it means that the target signal is present. On the other hand, a complete search without major weights estimate means that the target signal is absent. In addition, the overall system, named cyclic frequency lock loop can be reformed as a demodulator for sinusoidal frequency modulation signals. The method is very easy to implement and has less computational complexity comparing with its other spectrum sensing counterparts. Simulation results validate the efficiency of the proposed method.

关键词： Spectrum sensing wideband cyclostationary cyclic frequency nlms algorithm FM signals

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A variable-parameter normalized mixed-norm (VPNMN) adaptive algorithm

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EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2012年第1期2012卷 1页

作者： Zerguine, Azzedine King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

Since both the least mean-square (LMS) and least mean-fourth (LMF) algorithms suffer individually from the problem of eigenvalue spread, so will the mixed-norm LMS-LMF algorithm. Therefore, to overcome this problem for the mixed-norm LMS-LMF, we are adopting here the same technique of normalization (normalizing with the power of the input) that was successfully used with the LMS and LMF separately. Consequently a new normalized variable-parameter mixed-norm (VPNMN) adaptive algorithm is proposed in this study. This algorithm is derived by exploiting a time-varying mixing parameter in the traditional mixed-norm LMS-LMF weight update equation. The time-varying mixing parameter is adjusted according to a well-known technique used in the adaptation of the step-size parameter of the LMS algorithm. In order to study the theoretical aspects of the proposed VPNMN adaptive algorithm, our study also addresses its convergence analysis, and assesses its performance using the concept of energy conservation. Extensive simulation results corroborate our theoretical findings and show that a substantial improvement, in both convergence time and steady-state error, can be obtained with the proposed algorithm. Finally, the VPNMN algorithm proved its usefulness in a noise cancellation application where it showed its superiority over the normalized least-mean square algorithm.

关键词： LMS algorithm nlms algorithm LMF algorithm mixed-norm algorithms normalized mixed-norm algorithms

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Stochastic Analysis of a Stable Normalized Least Mean Fourth algorithm for Adaptive Noise Canceling With a White Gaussian Reference

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2012年第12期60卷 6235-6244页

作者： Eweda, Eweda Bershad, Neil J. Ajman Univ Sci & Technol Dept Elect Engn Ajman U Arab Emirates Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92660 USA

The least mean fourth (LMF) algorithm has several stability problems. Its stability depends on the variance and distribution type of the adaptive filter input, the noise variance, and the initialization of the filter weights. A global solution to these stability problems was presented recently for a normalized LMF (NLMF) algorithm. Here, a stochastic analysis of the mean-square deviation (MSD) of the globally stable NLMF algorithm is provided. The analysis is done in the context of adaptive noise canceling with a white Gaussian reference input and Gaussian, binary, and uniform desired signals. The analytical model is shown to accurately predict the results of Monte Carlo simulations. Comparisons of the NLMF and nlms algorithms are then made for various parameter selections. It is then shown under what conditions the NLMF algorithm is superior to nlms algorithm for adaptive noise canceling.

关键词： Adaptive filtering adaptive noise canceling least mean fourth algorithm nlms algorithm normalized least mean fourth algorithm

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Convergence vector of normalized least-mean-square algorithm for predicting deterministic sinusoidal signals

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ACOUSTICAL SCIENCE AND TECHNOLOGY 2012年第4期33卷 270-272页

作者： Wakabayashi, Yuko Satomi, Yuki Kawamura, Arata Iiguni, Youji Osaka Univ Grad Sch Engn Sci Machikaneyama 1-3 Toyonaka Osaka 5608531 Japan

We derived the convergence vector of the nlms algorithm for predicting deterministic sinusoidal signals. We showed that the derived convergence vector depends only on the initial vector and the sinusoidal frequencies.... 详细信息

关键词： nlms algorithm Linear prediction Sinusoidal signal Active noise control Line enhancer

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Efficient subband fast adaptive algorithm based-backward blind source separation for speech intelligibility enhancement

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2020年第2期23卷 471-479页

作者： Sayoud, Akila Djendi, Mohamed Univ Blida 1 Signal Proc & Image Lab LATSI Route SoumaaBP 270 Blida 09000 Algeria

This paper addresses the problem of speech intelligibility enhancement by subband adaptive filtering algorithms in a blind framework. Recently in Djendi and Sayoud (Int J Speech Technol 22:391-406, 2019), we have proposed a subband adaptive algorithm based on the forward blind source separation structure that is efficient for acoustic noise reduction and speech intelligibility enhancement applications. In this paper, we propose a novel subband domain implementation of the backward blind source separation structure combined with a modified version of the fast normalized least mean square (Fnlms) algorithm. The new proposed subband algorithm is efficient in improving the speech signal intelligibility without introducing any distortion at the output. A fair comparison of the proposed backward subband Fnlms algorithm with other fullband type algorithms is presented. This comparison is based on the evaluation of several objective criteria. The obtained results show the best performance of the proposed subband algorithm in terms of speed convergence.

关键词： BBSS Speech enhancement Subband adaptive filtering nlms algorithm SNR

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A Unified Performance Analysis of the Family of Normalized Least Mean algorithms

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第10期39卷 7145-7157页

作者： Moinuddin, Muhammad Zerguine, Azzedine King Abdulaziz Univ Elect & Comp Engn Dept Jeddah 21859 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah 21859 Saudi Arabia King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

This work presents a unified performance analysis of the family of normalized least mean (NLM) algorithms under very weak assumptions. The key feature of the analysis is based on a recently proposed performance measure called effective weight deviation vector. The name is so given as it is the only component that contributes to the excess estimation error of the adaptive filter. Using this novel concept, both the steady-state analysis and the tracking analysis are presented with a unified framework for the whole family of the NLM algorithms. Thus, the derived results are valid for any normalized stochastic gradient algorithm minimizing 2pth power of the error where p is an integer value. The novelty of the analysis resides in the fact that it does not impose restrictions on the dependence between input successive regressors, the dependence among input regressor elements, the length of the adaptive filter, the distribution of noise, and filter's input. Moreover, this approach is not limited to only small step-size value, and therefore, the analysis is valid for all the values of the step size in the stable range of the NLM algorithms. Furthermore, in this analysis, both stationary and non-stationary input signal and plant scenarios are considered. Consequently, asymptotic time-averaged convergence for the mean-squared effective weight deviation, mean absolute excess estimation error, and the mean-square excess estimation error for the NLM algorithm are established for both constant and time-varying plants. Finally, a number of simulation results are carried out to corroborate the theoretical findings.

关键词： LMS algorithm LMF algorithm nlms algorithm NLMF algorithm

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A switched variable step size nlms adaptive filter

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DIGITAL SIGNAL PROCESSING 2020年 101卷 102730-000页

作者： Bershad, Neil J. Bermudez, Jose C. M. Univ Calif Irvine Dept Elect & Comp Engn Irvine CA 92717 USA Univ Fed Santa Catarina Dept Elect & Elect Engn Florianopolis SC Brazil Catholic Univ Pelotas UCPel Pelotas Grad Program Elect Engn & Comp Pelotas RS Brazil

This paper studies the statistical behavior of a switched variable step size Normalized Least Mean Square adaptive filter. The purpose of the switching is to obtain a Normalized Least Mean Square adaptive filter combination with fast convergence and small steady-state mean-square weight deviation (MSD). Previous works studied variable step-size Least Mean Square (LMS) and Normalized Mean Square (nlms) adaptive filters using a measured quantity (say instantaneous squared error) in a nonlinear recursion for varying the step size. Here, it is proposed to avoid the nonlinear recursion in a novel way that is based upon a simple physical premise: For several practical applications, what is required is a faster initial convergence rate phase, followed by a slower convergence rate period that allows for a desired steady-state performance. The fast nlms transient behavior is obtained with a fixed step size. After convergence, the step size is reduced to a smaller value to satisfy a desired tradeoff between the second transient and the desired steady-state MSD. Thus, during the first transient, the Switched VSS-nlms adaptive weights will converge faster or as fast as any VSS-nlms algorithm. Of course, during the second phase, some variable step size algorithms may eventually outperform the Switched VSS-nlms algorithm. However, in many cases, this can be a small price to pay in exchange for the theoretical and computational simplicity. No new theory is needed to predict the behavior of the nonlinear recursion for the step size. Furthermore, the analytical theory for the MSD of the new scheme is well-known. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Adaptive filters Variable step-size nlms algorithm Stochastic algorithms

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IMPROVING THE CONVERGENCE OF ADAPTIVE HAMMERSTEIN FILTERS

IMPROVING THE CONVERGENCE OF ADAPTIVE HAMMERSTEIN FILTERS

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21st European Signal Processing Conference (EUSIPCO)

作者： Batista, Eduardo L. O. Seara, Rui Univ Fed Santa Catarina LINSE Circuits & Signal Proc Lab Dept Informat & Stat BR-88040900 Florianopolis SC Brazil Univ Fed Santa Catarina LINSE Circuits & Signal Proc Lab Dept Elect Engn BR-88040900 Florianopolis SC Brazil

ISBN: (纸本)9781479936878

The implementation of adaptive Hammerstein filters involves updating the coefficients of two cascaded blocks, namely, a memoryless nonlinearity and a linear filter. Such an update process presents important numerical problems mainly due to the non-uniqueness of the coefficient values that lead to optimum performance. These problems can be circumvented by keeping constant (not adapting) one of the filter coefficients, which however may significantly slow down the convergence of the adaptive algorithm. In this context, this paper presents a novel approach to implement adaptive Hammerstein filters in which a coefficient normalization strategy is used to overcome the aforementioned numerical problems. Thus, enhanced convergence speed is obtained with a small increase in the computational burden. Simulation results are presented to corroborate the effectiveness of the proposed strategy.

关键词： Adaptive filters Hammerstein filters nlms algorithm nonlinear filters

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Boring Bar Chatter Control using Feedback Filtered-x Normalized Least Mean Square algorithm 13th

Boring Bar Chatter Control using Feedback Filtered-x Normali...

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13th International-Federation-of-Automatic-Control (IFAC) Workshop on Intelligent Manufacturing Systems (IMS)

作者： Fallah, Mohsen Moetakef-Imani, Behnam Hosseini, Ali Ebrahimi, Mostafa Ferdowsi Univ Mashhad Dept Mech Engn CAD CAM Lab Mashhad Razavi Khorasan Iran Ontario Tech Univ Fac Engn & Appl Sci Machining Res Lab Oshawa ON Canada

This paper deals with the adaptive control of chatter vibrations in boring operations. A novel feedback control method is suggested based upon the well-known filtered-x normalized least mean square (Fxnlms) algorithm. The proposed feedback controller is implemented on the developed active boring bar that is equipped with electrodynamic shaker and accelerometer. The adaptive digital control algorithm utilizes two finite impulse response (FIR) filters in order to efficiently drive the electromagnetic actuator and successfully minimize the mean square of the chatter vibrations. Feasibility of the proposed control algorithm is verified for boring of Aluminum alloy 6063-T6. The performance of feedback Fxnlms controller is compared with the constant-gain direct velocity feedback (DVF) controller. It is observed that both control algorithms attenuate the chatter by more than 70 dB. In addition, the actuator cost is reduced by 50 percent for the optimally tuned adaptive digital controller. As a result, the proposed adaptive controller provides a robust and efficient solution for chatter suppression. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Chatter Signal processing Model based control Adaptive digital filters nlms algorithm

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Improvement in Convergence Speed and Stability of Least Mean Square and Normalized Least Mean Square algorithm 2

Improvement in Convergence Speed and Stability of Least Mean...

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2nd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Sharma, Divya Kaur, Rashpinder Chitkara Univ Rajpura India

ISBN: (纸本)9789380544168

This paper presents instigation of Least Mean Square and Normalized Least Mean Square algorithm on infinite impulse response filter using adaptive filter toolkit of LabVIEW software. User interface is designed using LabVIEW to obtain the learning curves for adaptive algorithms. The final results are comparing the performance of the least mean square algorithm with normalized least mean square algorithm. The complete performance of the designed system in terms of stability and convergence rate is observed. The standard least mean square algorithm possesses good speed of convergence as compared to the normalized least mean square algorithm.

关键词： Adaptive IIR Filter Digital Signal Processing FIR Filter LabVIEW LMS algorithm nlms algorithm

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