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2010 International Colloquium on Computing,Communication, Control, and Management (CCCM2010)

作者： WU Wei-wei Vehicle Engineering College Shanghai University of Engineering Science Shanghai,China WANG Yan-song Vehicle Engineering College Shanghai University of Engineering Science Shanghai,China

A wavelet transform based affine projection algorithm is proposed,with a variable step-size *** numerical simulations and engineering application verify the performance of the *** the same conditions,the proposed algorithm has a faster convergence and the same steady state MSE compare to normal affine projection *** means to achieve the same rate of convergence less order is needed by this algorithm, which counteract the computing expenses induced by wavelet *** means of variable step-size scheme,the proposed algorithm has a faster convergence rate and a lower steady state MSE or misadjustment than the corresponding normal variable step-size affine projection algorithms.

关键词： affine projection algorithm adaptive filter wavelet transform variable step-size

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ZA-APA with zero attractor controller selection criterion for sparse system identification

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SIGNAL IMAGE AND VIDEO PROCESSING 2018年第2期12卷 371-377页

作者： Radhika, S. Sivabalan, A. Sathyabama Univ Madras Tamil Nadu India

The zero attraction affine projection algorithm (ZA-APA) is one of the sparse APAs that are based on -norm penalty function. It provides faster convergence and lower steady-state error than the conventional APA when the system is sparse. Most of the analysis for attraction-type APA is normally based on white Gaussian assumption for the input. In this paper, a detailed performance analysis of the ZA-APA is performed using individual weight error variance (IWV) method. Using the IWV method, the condition for the convergence in mean and mean square error sense and the steady-state mean square deviation (MSD) error based on non-Gaussian input assumption is derived. Theoretical derivation reveals that the value of zero attractor controller plays a key role in the final steady-state error. Hence, a selection criterion for zero attractor controller based on the steady-state MSD error is proposed. Finally, simulations are performed to validate the analysis made in the context of unknown system identification.

关键词： affine projection algorithm Zero attraction APA Mean square deviation Convergence Nonzero weights Zero weights Zero attractor controller

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Improving the Performance of Multitask Diffusion APA via Controlled Inter-Cluster Cooperation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2020年第3期67卷 903-912页

作者： Gogineni, Vinay Chakravarthi Chakraborty, Mrityunjoy Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India Norwegian Univ Sci & Technol NTNU Dept Elect Syst N-7491 Trondheim Norway

In this paper, we consider the problem of estimating multiple parameter vectors over a sensor network in a multitasking framework and under temporally-correlated input conditions. For this, an efficient clustered multitask diffusion affine projection algorithm (APA) is proposed that enjoys both intra-cluster and inter-cluster cooperation via diffusion. It is, however, shown that while collaboration in principle is a useful step to enhance the performance of a network, uncontrolled mode of inter-cluster collaboration can at times be detrimental to its convergence performance, especially near steady-state. To overcome this, a controlled form of inter-cluster collaboration is proposed by means of a control variable which helps in maintaining the collaboration in right direction. The proposed controlled multitask strategy attains improved performance in terms of both transient and steady-state mean square deviation (MSD) vis-a-vis existing algorithms, as also confirmed by simulation studies. We carry out a detailed performance analysis of the proposed algorithm, obtain stability bounds for its convergence in both mean and mean-square senses, and derive expressions for the network level MSD. Simulation results reveal that the proposed scheme performs consistently well even in the absence of cluster information.

关键词： Multitask network distributed adaptive estimation block maximum norm adaptive diffusion networks affine projection algorithm

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Adaptive current decoupling control scheme based on online multi-parameter identification for high-speed permanent magnet synchronous motor in fuel cell

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 86卷 542-553页

作者： Wang, Kun Yan, Yuanlin Mao, Kun Zheng, Shiqiang Hao, MoHan Zhang, Yin Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Natl Inst Extremely Weak Magnet Field Infrastruct State Key Lab Tradit Chinese Med Syndrome Hangzhou 310051 Peoples R China Hangzhou Kuntai Maglev Technol Co Ltd Hangzhou Peoples R China

The precise control of centrifugal hydrogen compressor (CHC) driven by high-speed permanent magnet synchronous motor (PMSM) is the key to the stable and efficient operation of hydrogen fuel cell (HFC). In this paper, an adaptive current deviation decoupling control (CDDC) strategy based on variable step size affine projection algorithm (VSS-APAs) is proposed to solve the high-speed PMSM control problem caused by current cross- coupling and parameter perturbation under complex operating conditions. Firstly, the step size is dynamically adjusted by VSS-APA through a normalized gradient descent to respond to system fluctuations, achieving rapid parameter identification during dynamic changes and accurate tracking in steady states. Secondly, stator inductance, magnetic flux linkage, stator resistance, and disturbance torque are identified in two-time-scale based on the characteristics of parameter changes, overcoming the rank deficiency in motor mathematical equations. Finally, utilizing online-identified motor parameters, the gains of the CDDC are adjusted, and compensation is applied for back electromotive force (BEMF) and disturbance torque, thereby ensuring the control accuracy of the motor under parameter perturbations and system disturbances. Experimental results demonstrate that the proposed method enhances the responsiveness and accuracy of the control strategy through efficient parameters identification. Consequently, it guarantees high-efficiency and stable performance of the CHC across different working conditions, markedly advancing the overall functionality and dependability of hydrogen energy system.

关键词： Centrifugal hydrogen compressor Permanent magnet synchronous motor Current decoupling control affine projection algorithm Online parameter identification Adaptive control

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A Comparative Survey of Convex Combination of Adaptive Filters

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IETE JOURNAL OF RESEARCH 2023年第2期69卷 940-950页

作者： Vazquez, Angel A. Avalos, J. Gerardo Sanchez, Giovanny Sanchez, Juan C. Perez, Hector Inst Politecn Nacl Mexico City 04260 DF Mexico Inst Politecn Nacl Grad Studies & Res Sect ESIME Culhuacan Mexico City 04260 DF Mexico

Over the last twenty years, combination of adaptive filters has emerged to provide a potential solution in the development of advanced applications, such as channel equalization, active noise control, acoustic echo cancelation, adaptive beamforming, among others. Several authors have demonstrated that the combination of two filters with complementary capabilities improves the overall filter performance in comparison when a single filter is used. Commonly, these combinations employ a fast filter and slow filter to guarantee fast convergence speed and low steady-state mean square error (MSE), respectively, at the cost of increasing their computational complexity. However, those combinations have not been well analysed and compared. Currently, this aspect is crucial since most of the current convex combination of adaptive filters could be implemented in advanced embedded digital devices to be used in real-time signal processing applications. In this work, we evaluate the convex combination of adaptive filters, mainly including those based on least mean square (LMS) algorithm, the affine projection (AP) algorithm and the recursive least mean square (RLS) algorithm. We carry out an extensive performance evaluation to demonstrate the advantages and disadvantages of each approach. Specifically, we simulate them in fixed point, which makes feasible the rapid development of advanced prototypes. This aspect could be potentially valuable to the signal processing engineering communities.

关键词： Adaptive filters Adaptation term affine projection algorithm Convex combination Least mean square algorithm Recursive least mean square algorithm

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An efficient robust adaptive filtering algorithm based on parallel subgradient projection techniques

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2002年第5期50卷 1091-1101页

作者： Yamada, I Slavakis, K Yamada, K Tokyo Inst Technol Dept Commun & Integrated Syst Tokyo 152 Japan

This paper presents a novel robust adaptive filtering scheme based on the interactive use of statistical noise information and the ideas developed originally for efficient algorithmic solutions to the convex feasibility problems. The statistical noise information is quantitatively formulated as stochastic property closed convex sets by the simple design formulae developed in this paper. A simple set-theoretic inspection also leads to an important statistical reason for the sensitivity to noise of the affine projection algorithm (APA). The proposed adaptive algorithm is computationally efficient and robust to noise because it requires only an iterative parallel projection onto a series of closed half spaces that are highly expected to contain the unknown system to be identified and is free from the computational load of solving a system of linear equations. The numerical examples show that the proposed adaptive filtering scheme realizes dramatically fast and stable convergence for highly colored excited speech like input signals in severe noise situations.

关键词： affine projection algorithm NLMS algorithm POCS robust adaptive algorithm stochastic property set subgradient projection

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An adaptive convex combination of APA and ZA-APA for identifying systems having variable sparsity and correlated input

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DIGITAL SIGNAL PROCESSING 2018年 82卷 118-132页

作者： Gogineni, Vinay Chakravarthi Das, Bijit K. Chakraborty, Mrityunjoy Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India

In this paper, we present an efficient algorithm for identifying and tracking the impulse response of a sparse system that exhibits time varying sparseness and is driven by correlated input. The proposed method convexly combines the outputs of two filters, namely, the sparsity unaware affine projection algorithm (APA), and the sparsity aware zero attracting affine projection algorithm (ZA-APA), each trying to identify the same system using the same input. The combining parameter is adapted by following a steepest descent of the error variance at the convex combination output. A detailed performance analysis of the proposed combination is carried out, which reveals that while for highly non-sparse and highly sparse systems, the proposed combination converges respectively to the APA and ZA-APA (i.e., better of the two filters under the given levels of sparsity), for certain sparsity ranges, it leads to a combination filter that performs better than both the constituent filters. The claims made are validated by exhaustive simulation studies using white, colored as well as speech inputs. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Sparse systems l(1)-norm Excess mean square error Adaptive filter affine projection algorithm

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Adaptive filtering algorithms with selective partial updates

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2001年第8期48卷 762-769页

作者： Dogançay, K Tanrikulu, O Univ S Australia Sch Elect & Informat Engn Mawson Lakes SA 5095 Australia Tellabs Media Proc Technol Bedford MA 01730 USA

In some applications of adaptive filtering such as active noise reduction, and network and acoustic echo cancellation, the adaptive filter may be required to have a large number of coefficients in order to model the unknown physical medium with sufficient accuracy. The computational complexity of adaptation algorithms is proportional to the number of filter coefficients. This implies that, for long adaptive filters, the adaptation task can become prohibitively expensive, ruling out cost-effective implementation on digital signal processors. The purpose of partial coefficient updates is to reduce the computational complexity of an adaptive filter by adapting a block of the filter coefficients rather than the entire filter at every iteration. In this paper, we develop a selective-partial-update normalized least-mean-square (NI,MS) algorithm, and analyze its stability using the traditional independence assumptions and error-energy bounds. Selective partial updating is also extended to the affine projection (AP) algorithm by introducing multiple constraints. The new algorithms appear to have good convergence performance as attested to by computer simulations with real speech signals.

关键词： acoustic echo cancellation adaptive filters affine projection algorithm normalized least-mean-square algorithm partial updating

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A Bias-Free Adaptive Beamformer with GSC-APA

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2015年第6期E98A卷 1295-1299页

作者： Han, Yun-Ki Lee, Jae-Woo Lee, Han-Sol Song, Woo-Jin POSTECH Div Elect & Comp Engn Pohang South Korea

We propose a novel bias-free adaptive beamformer employing an affine projection algorithm with the optimal regularization parameter. The generalized sidelobe canceller affine projection algorithm suffers from a bias of a weight vectors under the condition of no reference signals for output of an array in the beamforming application. First, we analyze the bias in the algorithm and prove that the bias can be eliminated through a large regularization parameter. However, this causes slow convergence at the initial state, so the regularization parameter should be controlled. Through the optimization of the regularization parameter, the proposed method achieves fast convergence without the bias at the steady-state. Experimental results show that the proposed beamformer not only removes the bias but also achieves both fast convergence and high steady-state output signal-to-interference-plus-noise ratio.

关键词： adaptive beamformer generalized sidelobe canceller affine projection algorithm bias-compensation

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New Insights into the Kalman Filter Beamformer: Applications to Speech and Robustness

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IEEE SIGNAL PROCESSING LETTERS 2016年第3期23卷 376-380页

作者： Cherkassky, Dani Gannot, Sharon Bar Ilan Univ Fac Engn IL-5290002 Ramat Gan Israel

Statistically optimal spatial processors (also referred to as data-dependent beamformers) are widely-used spatial focusing techniques for desired source extraction. The Kalman filter-based beamformer (KFB) [1] is a recursive Bayesian method for implementing the beamformer. This letter provides new insights into the KFB. Specifically, we adopt the KFB framework to the task of speech extraction. We formalize the KFB with a set of linear constraints and present its equivalence to the linearly constrained minimum power (LCMP) beamformer. We further show that the optimal output power, required for implementing the KFB, is merely controlling the white noise gain (WNG) of the beamformer. We also show, that in static scenarios, the adaptation rule of the KFB reduces to the simpler affine projection algorithm (APA). The analytically derived results are verified and exemplified by a simulation study.

关键词： Adaptive beamformer affine projection algorithm Kalman filter LCMP beamformer microphone arrays speech enhancement speech extraction

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