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Performance bounds of distributed adaptive filters with cooperative correlated signals

Science China(Information Sciences) 2016年第11期59卷 53-62页

作者： Chen CHEN Zhixin LIU Lei GUO Shannon Cognitive Computing Laboratory 2012 Lab Huawei Technologies Co.Ltd. Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences

In this paper, we studied the least mean-square-based distributed adaptive filters, aiming at collectively estimating a sequence of unknown signals(or time-varying parameters) from a set of noisy measurements obtained through distributed sensors. The main contribution of this paper to relevant literature is that under a general stochastic cooperative signal condition, stability and performance bounds are established for distributed filters with general connected networks without stationarity or independency assumptions imposed on the regression signals.

关键词： distributed adaptive filters LMS random process stochastic stability graph connectivity

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Analysis of distributed adaptive filters Based on Diffusion Strategies Over Sensor Networks

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2018年第11期63卷 3643-3658页

作者： Xie, Siyu Guo, Lei Chinese Acad Sci Key Lab Syst & Control Acad Math & Syst Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China

In this paper, we will analyze a basic class of diffusion adaptive filters based on least mean squares algorithms. Both stability and performance analyses will be carried out under a general cooperative information condition, without such stringent conditions as statistical independence and stationarity that have been used in almost all the existing literature and, thus, makes our theory applicable to stochastic systems with feedback. In comparison with the existing work, a key theoretical difficulty that needs to be overcome in this paper is to analyze the product of asymmetric correlated non-stationary random matrices, which is inherent in the structure of the diffusion-type filtering algorithms. We will further demonstrate that the distributed adaptive filters can estimate a dynamic process of interest from noisy measurements by a set of sensors working in a cooperative way, in the natural scenario where none of the sensors can fulfill the estimation task individually due to insufficient information. Finally, the necessity of our cooperative information condition will also be discussed in this paper.

关键词： Diffusion strategies distributed adaptive filters least mean squares stochastic stability tracking performance

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Exponential Stability of LMS-Based distributed adaptive filters

Exponential Stability of LMS-Based Distributed Adaptive Filt...

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20th World Congress of the International-Federation-of-Automatic-Control (IFAC)

作者： Xie, Siyu Guo, Lei Chinese Acad Sci Inst Syst Sci AMSS Beijing 100190 Peoples R China

In this work, we consider a class of distributed adaptive filters based on the standard least mean squares (LMS) algorithm, which is proposed to track an unknown signal process in sensor networks. We analyze the stability by introducing a stochastic cooperative information (SCI) condition, in the case of non-independent, non-stationary and possibly unbounded signals. Under the SCI condition, the distributed adaptive filters based on the standard LMS will be shown to be able to track a dynamic process of interest from noisy measurements by a set of sensors working collaboratively, in the natural scenario where any sensor cannot fulfil the estimation task individually. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Least mean squares distributed adaptive filters L-p-exponentially stability graph topology stochastic averaging stochastic cooperative information

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Analysis of compressed distributed adaptive filters

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AUTOMATICA 2020年 112卷

作者： Xie, Siyu Guo, Lei Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

In order to estimate an unknown high-dimensional sparse signal in the network, we present a class of compressed distributed adaptive filtering algorithms based on consensus strategies and compressive sensing (CS) method. This class of algorithms is designed to first use the compressed regressor data to obtain an estimate for the compressed unknown signal, then apply some signal reconstruction algorithms to obtain a high-dimensional estimate for the original unknown signal. Here we consider the compressed consensus normalized least mean squares (NLMS) algorithm, and show that even if the traditional non-compressed distributed algorithm cannot fulfill the estimation or tracking task due to the sparsity of the regressors, the compressed algorithm introduced in this paper can be used to estimate the unknown high-dimensional sparse signal under a compressed information condition, which is much weaker than the cooperative information condition used in the existing literature, without such stringent conditions as independence and stationarity for the system signals. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Sparse signal Compressive sensing distributed adaptive filters Consensus strategies Compressed information condition Stochastic stability

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Selective collaboration in distributed FxLMS active noise control systems

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DIGITAL SIGNAL PROCESSING 2025年 156卷

作者： Garcia-Molly, Victor M. Ferrer, Miguel de Diego, Maria Gonzalez, Alberto Univ Politecn Valencia UPV Dept Informat Syst & Comp DSIC Valencia 46022 Spain Univ Politecn Valencia UPV Inst Telecommun & Multimedia Applicat iTEAM Valencia 46022 Spain

This paper addresses the selection of the collaboration configuration on distributed active noise control (ANC) systems. ANC systems aim to cancel out acoustic noise within a listening area. In distributed systems, the control task is delegated among multiple acoustic control nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated using the filtered-X LMS algorithm. The stability is achieved when the adaptive filters computed in each node converge to finite values. However, acoustic coupling among nodes could lead to instability (i.e., divergence). Collaboration among selected nodes may avoid this phenomenon, although not just any collaboration configuration guarantees network stability. On the other hand, a collaborative distributed system presents two drawbacks: the stability assessment is computationally expensive, and communication requirements increase with the number of collaborations among nodes. In this paper, we propose and discuss several methods to establish a collaboration configuration that ensures system stability. The optimal configuration, which is characterized by the minimal number of necessary collaborations between nodes, can be identified through exhaustive search. However, this approach incurs a high computational cost, particularly in networks with many nodes. To address this challenge, we introduce several heuristic methods aimed at efficiently obtaining stable configurations.

关键词： Acoustic sensor networks distributed adaptive filters Active noise control Eigenvalue calculation Stability analysis

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Diffusion-Based distributed Parameter Estimation Through Directed Graphs With Switching Topology: Application of Dynamic Regressor Extension and Mixing

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第8期67卷 4256-4263页

作者： Matveev, Alexey S. Almodarresi, Mostafa Ortega, Romeo Pyrkin, Anton Xie, Siyu St Petersburg State Univ Dept Math & Mech St Petersburg 198504 Russia Yazd Univ Dept Elect Engn Yazd 89195741 Iran ITAM Dept Acad Sistemas Digit Ciudad De Mexico 01080 Mexico ITMO Univ Dept Control Syst & Informat St Petersburg 197101 Russia Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

In this article, we consider the problem of discrete-time, diffusion-based distributed parameter estimation with the agents connected via directed graphs with switching topologies and a self loop at each node. We show that, by incorporating the recently introduced dynamic regressor extension and mixing procedure to a classical gradient-descent algorithm, improved convergence properties can be achieved. In particular, it is shown that with this modification sufficient conditions for global convergence of all the estimators is that one of the sensors receives enough information to generate a consistent estimate and that this sensor is "well-connected." The main feature of this result is that the excitation condition imposed on this distinguished sensor is strictly weaker than the classical persistent excitation requirement. The connectivity assumption is also very mild, requiring only that the union of the edges of all connectivity graphs over any time interval with an arbitrary but fixed length contains a spanning tree rooted at the information-rich node. In the case of nonswitching topologies, this assumption is satisfied by strongly connected graphs, and not only by them.

关键词： Sensors Estimation Topology Network topology Convergence Sensor fusion Directed graphs Consensus algorithms diffusion strategies distributed adaptive filters dynamic regressor extension and mixing sensor networks

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distributed Affine Projection Algorithm Over Acoustically Coupled Sensor Networks

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2017年第24期65卷 6423-6434页

作者： Ferrer, Miguel Gonzalez, Alberto de Diego, Maria Pinero, Gema Univ Politecn Valencia Inst Telecommun & Multimedia Applicat E-46022 Valencia Spain

In this paper, we present a distributed affine projection (AP) algorithm for an acoustic sensor network where the nodes are acoustically coupled. Every acoustic node is composed of a microphone, a processor, and an actuator to control the sound field. This type of networks can use distributed adaptive algorithms to deal with the active noise control (ANC) problem in a cooperative manner, providing more flexible and scalable ANC systems. In this regard, we introduce here a distributed version of the multichannel filtered-x AP algorithm over an acoustic sensor network that it is called distributed filtered-x AP (DFxAP) algorithm. The analysis of the mean and the mean-square deviation performance of the algorithm at each node is given for a network with a ring topology and without constraints in the communication layer. The theoretical results are validated through several simulations. Moreover, simulations show that the proposed DFxAP outperforms the previously reported distributed multiple error filtered-x least mean square algorithm.

关键词： Acoustic sensor networks affine projection algorithm distributed adaptive filters active noise control

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Diffusion adaptive networks with changing topologies

Diffusion adaptive networks with changing topologies

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33rd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Lopes, Cassio G. Sayed, Ali H. Univ Calif Los Angeles Dept Elect Engn Los Angeles CA 90095 USA

ISBN: (纸本)9781424414833

adaptive networks (AN) have been recently proposed to address distributed estimation problems [1]-[4]. Here we extend prior work to changing topologies and data-normalized algorithms. The resulting framework may also treat signals with general distributions, rather than Gaussian, provided that certain data statistical moments are known. A byproduct of this formulation is a probabilistic diffusion adaptive network: a simpler yet robust variant of the standard diffusion algorithm [2].

关键词： adaptive filters distributed estimation adaptive networks diffusion cooperative systems distributed adaptive filters

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Analysis of Consensus adaptive filters Over Directed and Markovian Switching Networks 13

Analysis of Consensus Adaptive Filters Over Directed and Mar...

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13th Asian Control Conference (ASCC)

作者： Xie, Siyu Wang, Le Yi Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 611731 Peoples R China

ISBN: (纸本)9788993215236

The problem of distributed adaptive filtering is considered in this paper, where all the nodes in the network will estimate an unknown parameter from noisy measurements cooperatively. In this work, we mainly focus on consensus strategies and normalized least mean squares (NLMS)-based adaptive filters, where the network is described by directed and Markovian switching graphs. The stability results of the above distributed adaptive filters are established under directed Markovian switching networks and a general cooperative information assumption, without independence and stationarity signal conditions which are widely used in the literature. Moreover, the stability results also indicate that even any node can not estimate the unknown parameter individually, the estimation task can still be fulfilled through local communications among nodes in the network.

关键词： distributed adaptive filters consensus strategies least mean squares directed graphs Markovian switching stochastic stability

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Assessment of stability of distributed FxLMS active noise control systems *

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SIGNAL PROCESSING 2023年第1期210卷

作者： Ferrer, Miguel Garcia-Molla, Victor M. Vidal-Macia, Antonio M. de Diego, Maria Gonzalez, Alberto Univ Politecn Valencia UPV Inst Telecommun & Multimedia Applicat iTEAM Valencia 46022 Spain Univ Politecn Valencia UPV Dept Informat Syst & Comp DSIC Valencia 46022 Spain

This paper addresses the assessment of the stability of distributed active noise control (ANC) systems, which are designed to cancel acoustic noise at given points in space. These systems distribute the control task across several simple acoustic nodes that generate the control signals by filtering a noise reference signal. The coefficients of each node filter are iteratively calculated by the filtered-X LMS algorithm. The nodes remain stable when the adaptive filters computed in each node converge to finite values. However, the acoustic coupling among nodes can cause instability (i.e., divergence). Collaboration among nodes is required to avoid this phenomenon. It is shown that the properties of the system are summarized in a system matrix and that the system remains stable when the real parts of all of the eigenvalues of this system matrix are positive. However, computation of all of the eigenvalues is computationally expensive. In this paper, we propose a fast method for checking the positiveness of the real parts of the system ma-trix eigenvalues. It is shown that the proposed method is faster than the direct calculation of eigenvalues and it assesses the stability/instability of the ANC systems without any false stability outcomes and more accurately than existing alternatives. & COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://***/licenses/by-nc-nd/4.0/ )

关键词： Acoustic sensor networks distributed adaptive filters Active noise control Eigenvalue calculation stability analysis

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