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5th IFAC Symposium on Telematics Applications (TA)

作者： Song, Pucha Zhao, Haiquan Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Sichuan Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Sichuan Peoples R China

In this paper, a robust diffusion affine projection M-estimate (DAPM) algorithm is proposed for distributed estimation in the adaptive diffusion network. To eliminate the adverse effects of impulsive noise in case of the impulsive interference environment on the filter weight updates, this algorithm uses a robust cost function based on M-estimate function and is derived by the steepest-descent method. Simulation results verify that the proposed DAPM algorithm is effective for system identification scenarios in the presence of impulsive noise.

关键词： affine projection algorithm M-estimate distributed estimation adaptive diffusion network impulsive noise

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Distributed Estimation Over an Adaptive Diffusion Network Based on the Family of affine projection algorithms

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IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS 2019年第2期5卷 234-247页

作者： Abadi, Mohammad Shams Esfand Shafiee, Mohammad Saeed Shahid Rajaee Teacher Training Univ Fac Elect Engn Tehran *** Iran

This paper utilizes the family of Aline projection algorithms (APAs) for distributed estimation in the adaptive diffusion networks. The diffusion APA (DAPA), the diffusion selective partial update (SPU) APA (DSPU-APA), the diffusion selective regressor (SR) APA (DSR-APA), and the diffusion dynamic selection (DS) APA (DDS-APA) are introduced in a unified way. In DSPU-APA, the weight coefficients are partially updated at each node during the adaptation. Therefore, the DSPU-APA has lower computational complexity in comparison to the DAPA. In addition, the convergence speed of the DSPU-APA is close to the DAPA. In DSR-APA, a subset of input regressors is optimally selected at each node during the adaptation. The dynamic selection of input regressors is performed in the DDS-APA. These strategies improve the performance of the conventional DAPA in terms of the steady-state error and computational complexity features. Also, by combining these algorithms, the DSPU-SR-APA and the DSPU-DS-APA are established, which are computationally efficient. The mean-square performance of the proposed algorithms is analyzed in the nonstationary environment and the generic relations for the theoretical learning curve and the steady-state error are derived. The analysis is based on the spatial-temporal energy conservation relation. The validity of the theoretical results and the good performance of the introduced algorithms are demonstrated by several computer simulations in diffusion networks.

关键词： affine projection algorithm distributed estimation diffusion network nonstationary mean-square performance

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Convergence analysis of block orthogonal projection and affine projection algorithms

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SIGNAL PROCESSING 2002年第3期82卷 491-496页

作者： Ikeda, K Kyoto Univ Dept Syst Sci Grad Sch Informat Kyoto 6068501 Japan

The affine projection (AP) and the block orthogonal projection (BOP) algorithms are known to have good convergence properties even when the input signal is correlated. However, their convergence rates are not elucidated yet, since they are based on orthogonal projection. and are too complicated to be analyzed. In this paper, we first consider their geometrical view and derive their convergence rates for white signals. Moreover, we provide an approximation method to evaluate the convergence rate for cot-related signals and show the results which are confirmed by computer simulations. The results imply that the convergence rate of the BOP algorithm gets saturated as the block size increases. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： adaptive filter convergence rate normalized LMS algorithm affine projection algorithm block orthogonal projection algorithm

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Statistical convergence behavior of affine projection algorithms

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APPLIED MATHEMATICS AND COMPUTATION 2015年 270卷 511-526页

作者： Zhi, Yongfeng Li, Jieliang Zhang, Jun Wang, Zhen Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

Class of algorithms referring to the affine projection algorithms (APA) applies updates to the weights in a direction that is orthogonal to the most recent input vectors. This speeds up the convergence of the algorithm over that of the normalized least mean square (NLMS) algorithm, especially for highly colored input processes. In this paper a new statistical analysis model is used to analyze the APA class of algorithms with unity step size. Four assumptions are made, which are based on the direction vector for the APA class. Under these assumptions, deterministic recursive equations for the weight error and for the mean-square error are derived. We also analyze the steady-state behavior of the APA class. The new model is applicable to input processes that are autoregressive as well as autoregressive-moving average, and therefore is useful under more general conditions than previous models for prediction of the mean square error of the APA class. Simulation results are provided to corroborate the analytical results. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Adaptive filter affine projection algorithm Statistical analysis System identification

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Mean-square performance of a family of affine projection algorithms

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2004年第1期52卷 90-102页

作者： Shin, HC Sayed, AH Pohang Univ Sci & Technol Div Elect & Comp Engn Pohang South Korea Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

affine projection algorithms are useful adaptive filters whose main purpose is to speed the convergence of LMS-type filters. Most analytical results on affine projection algorithms assume special regression models or Gaussian regression data. The available analysis also treat different affine projection filters separately. This paper provides a unified treatment of the mean-square error, tracking, and transient performances of a family of affine projection algorithms. The treatment relies on energy conservation arguments and does not restrict the regressors to specific models or to a Gaussian distribution. Simulation results illustrate the analysis and the derived performance expressions.

关键词： affine projection algorithm energy-conservation learning-curve steady-state analysis tracking analysis transient analysis

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Low computational complexity family of affine projection algorithms over adaptive distributed incremental networks

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AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2014年第2期68卷 97-110页

作者： Abadi, Mohammad Shams Esfand Danaee, Ali-Reza Shahid Rajaee Teacher Training Univ Fac Elect & Comp Engn Tehran Iran

This paper presents the problem of distributed estimation in an incremental network based on the family of affine projection (AP) adaptive algorithms. The distributed selective partial update normalized least mean squares (dSPU-NLMS), the distributed SPU-AP algorithm (dSPU-APA), the distributed selective regressor APA (dSR-APA), the distributed dynamic selection of APA (dDS-APA), dSPU-SR-APA and dSPU-DS-APA are introduced in a unified way. These algorithms have low computational complexity feature and close convergence speed to ordinary distributed adaptive algorithms. In dSPU-NLMS and dSPU-APA, the weight coefficients are partially updated at each node during the adaptation. In dSR-APA, the optimum number of input regressors is selected during the weight coefficients update. The dynamic selection of input regressors is used in dDS-APA. dSPU-SR-APA and dSPU-DS-APA combine SPU with SR and DS approaches. In these algorithms, the weight coefficients are partially updated and the input regressors are optimally/dynamically selected at every iteration for each node. In addition, a unified approach for mean-square performance analysis of each individual node is presented. This approach can be used to establish a performance analysis of classical distributed adaptive algorithms as well. The theoretical expressions for stability bounds, transient, and steady-state performance analysis of various distributed APAs are introduced. The validity of the theoretical results and the good performance of dAPAs are demonstrated by several computer simulations. (C) 2013 Elsevier GmbH. All rights reserved.

关键词： affine projection algorithm Adaptive distributed estimation Incremental network Selective partial update Dynamic selection Mean-square performance

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Analysis of affine projection Normalized Correlation algorithm

Analysis of Affine Projection Normalized Correlation Algorit...

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International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS)

作者： Koike, Shin'ichi Tokyo Japan

ISBN: (纸本)9781509006298

This paper proposes and develops a statistical analysis of affine projection Normalized Correlation algorithm (AP-NCA) which is a combination of the affine projection algorithm (APA) and Normalized Correlation algorithm (NCA) for use in complex-domain adaptive filters. For impulse noise, two types are considered: one is found in observation noise and another at filter input. Through experiments with simulations and theoretical calculations of filter convergence, we demonstrate that the proposed AP-NCA is effective in making adaptive filters converge faster when the filter inputs are correlated, and robust in impulsive noise environments. It is observed that the theoretical convergence curves generally exhibit good agreement with the simulation results which shows that the analysis is valid for practical use.

关键词： adaptive filter affine projection algorithm normalized correlation algorithm impulse noise fast convergence robust filtering

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Improved affine projection algorithms with selective projection order for channel identification

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SIGNAL IMAGE AND VIDEO PROCESSING 2024年第12期18卷 8645-8657页

作者： Bekrani, Mehdi Zayyani, Hadi Mohagheghian-Bidgoli, Zahra Qom Univ Technol QUT Dept Elect & Comp Engn Khodakaram Blvd Qom Iran

One of the most significant challenges in adaptive filters is the slow convergence speed of the adaptive algorithm when dealing with highly correlated input signals. The adaptive affine projection algorithm (APA) which is a generalized version of the well-known normalized least mean square algorithm, improves convergence speed against correlated input signals, but with a high steady-state weight error and considerable computational complexity. In this paper, to enhance the convergence performance of APA and reduce its complexity, a selective projection order APA and its proportionate variant are proposed. In these algorithms, the projection order varies during convergence. Initially, a high projection order is employed in adaptation, gradually decreasing as the mean-square deviation (MSD) of the weight error decreases. This adaptive approach maintains a high initial convergence speed while reducingss the steady-state error. Moreover, the computational complexity decreases during convergence. Additionally, we propose a combination of proportionate and non-proportionate adaptive algorithms, leveraging the variation of MSD during convergence. Simulation results in identification of sparse/dispersive channels confirm the improved convergence performance of the proposed algorithms compared to competing adaptive algorithms.

关键词： Adaptive filter System identification affine projection algorithm Convergence rate

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Transient and steady-state analysis of filtered-x affine projection algorithms

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2006年第2期54卷 665-678页

作者： Carini, A Sicuranza, GL Univ Urbino Informat Sci & Technol Inst I-61029 Urbino Italy Univ Trieste Dept Elect Elect & Comp Engn Trieste Italy

This paper provides an analysis of transient and steady-state behavior of different filtered-x affine projection algorithms. algorithms suitable for single-channel and for multichannel active noise controllers are treated within a unified framework. Very mild assumptions are posed on the active noise control system model, which is only required to have a linear dependence of the output from the filter coefficients. Therefore, the analysis applies not only to the linear finite impulse response models but also to nonlinear Volterra filters, i.e., polynomial filters, and other nonlinear filter structures. The convergence analysis presented in this paper relies on energy conservation arguments and does not apply the independence theory, nor does it impose any restriction to the signal distributions. It is shown in the paper that filtered-x affine projection algorithms always provide a biased estimate of the minimum mean square solution. Nevertheless, in many cases, the bias is small and therefore these algorithms can be profitably applied to active noise control.

关键词： active noise control affine projection algorithm multichannel adaptive filtering steady-state analysis transient analysis

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Derivation of excess mean-square error for affine projection algorithms using the condition number

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2008年第9期E91A卷 2675-2677页

作者： Lee, Chang Woo Cho, Hyeonwoo Kim, Sang Woo POSTECH Dept Elect & Elect Engn Pohang South Korea

This letter presents a new mathematical expression for the excess mean-square error (EMSE) of the affine projection (AP) algorithm. The proposed expression explicitly shows the proportional relationship between the EM... 详细信息

关键词： affine projection algorithm excess mean square error condition number

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