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distributed parameter estimation in wireless sensor networks in the presence of fading channels and unknown noise variance

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INTERNATIONAL JOURNAL OF distributed SENSOR NETWORKS 2018年第9期14卷 1-14页

作者： Liu, Shoujun Liu, Kezhong Ma, Jie Chen, Wei Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Hubei Peoples R China Wuhan Univ Technol Sch Nav Wuhan Hubei Peoples R China

parameter estimation is one of the most important research areas in wireless sensor networks. In this study, we consider the problem of estimating a deterministic parameter over fading channels with unknown noise variance. Owing to the bandwidth constraints in wireless sensor networks, sensor observations are quantized and subsequently transmitted to the fusion center. Two types of communication channels are considered, namely, parallel-access channels and multiple-access channels. Based on the knowledge of channel statistics, the power of the received signals at the fusion center can be described by the mode of the exponential mixture distribution. The expectation maximization algorithm is used to determine maximum likelihood solutions for this mixture model. A new estimator based on the expectation maximization algorithm is subsequently proposed. Simulation results show that this estimator exhibits superior performance compared to the method of moments estimator in both parallel- and multiple-access schemes. In addition, we determine that the parallel-access scheme outperforms the multiple-access scheme when the noise variance is small and it loses its superiority when the noise variance is large.

关键词： distributed parameter estimation wireless sensor networks expectation maximization algorithm parallel-access channels multiple-access channels

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distributed parameter estimation in unreliable sensor networks via broadcast gossip algorithms

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NEURAL NETWORKS 2016年 73卷 1-9页

作者： Wang, Huiwei Liao, Xiaofeng Wang, Zidong Huang, Tingwen Chen, Guo Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England Texas A&M Univ Qatar Doha 5825 Qatar Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia

In this paper, we present an asynchronous algorithm to estimate the unknown parameter under an unreliable network which allows new sensors to join and old sensors to leave, and can tolerate link failures. Each sensor has access to partially informative measurements when it is awakened. In addition, the proposed algorithm can avoid the interference among messages and effectively reduce the accumulated measurement and quantization errors. Based on the theory of stochastic approximation, we prove that our proposed algorithm almost surely converges to the unknown parameter. Finally, we present a numerical example to assess the performance and the communication cost of the algorithm. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Broadcast gossip algorithm distributed parameter estimation Quantized communication Unreliable sensor networks

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A distributed control and parameter estimation protocol with prescribed performance for homogeneous lagrangian multi-agent systems

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AUTONOMOUS ROBOTS 2018年第8期42卷 1525-1541页

作者： Bechlioulis, Charalampos P. Demetriou, Michael A. Kyriakopoulos, Kostas J. Natl Tech Univ Athens Sch Mech Engn Control Syst Lab 9 Heroon Polytech Str Athens 15780 Greece Worcester Polytech Inst Dept Mech Engn Worcester MA 01609 USA

In this paper, we consider the formation control problem for uncertain homogeneous Lagrangian nonlinear multi-agent systems in a leader-follower scheme under a directed communication protocol. A distributed adaptive control protocol of minimal complexity is proposed that achieves prescribed, arbitrarily fast and accurate formation establishment as well as synchronization of the parameter estimates of all followers. The estimation and control laws are distributed in the sense that the control signal and the update laws are calculated based solely on local relative state information. Moreover, provided that the communication graph is strongly connected and contrary to the related works on multi-agent systems, the controller-imposed transient and steady state performance bounds are fully decoupled from: (i) the underlying graph topology, (ii) the control gains selection and (iii) the agents' model uncertainties. Finally, extensive simulation studies on the attitude control of flying spacecrafts clarify and verify the approach.

关键词： Networked lagrangian systems distributed formation control distributed parameter estimation Prescribed performance control

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Adjoint-based state and distributed parameter estimation in a switched hyperbolic overland flow model

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IFAC-PapersOnLine 2016年第18期49卷 205-210页

作者： Nguyen, Van Tri Georges, Didier Besançon, Gildas Univ. Grenoble Alpes GIPSA-Lab GrenobleF-38000 France

This paper considers the issue of state and parameter estimation in an overland flow model, including unknown infiltration coefficients. The overland flow dynamics are described by the continuity Saint-Venant equation, and the infiltration by the Green-Ampt model. Due to the related so-called ‘ponding time’, the overall model results in a switched partial differential equation, coupled with an ordinary differential one. In this model, the initial state, the friction coefficient and the infiltration parameters are assumed to be unknown, with only a discrete number of measurements being available. For the estimation, the model is modified by adding an activation function, and then used in the minimization of a cost function defined as the difference between available measurements and the corresponding simulated ones. The variational analysis is applied on the augmented Lagrangian objective functional in order to get the weak form of gradients of this function with respect to the variables to be estimated. Based on these gradients a quasi Newton method is used to solve the optimization problem. An illustration example is finally provided to validate the proposed approach. © 2016

关键词： parameter estimation Constrained optimization Cost functions Friction Inverse problems Newton Raphson method State estimation Adjoint methods distributed parameter estimation Green Ampt model Hyperbolic system Overland flow modeling Saint Venant Switched system

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Adjoint-based state and distributed parameter estimation in a switched hyperbolic overland flow model

Adjoint-based state and distributed parameter estimation in ...

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10th IFAC Symposium on Nonlinear Control Systems (NOLCOS)

作者： Van Tri Nguyen Georges, Didier Besancon, Gildas Univ Grenoble Alpes GIPSA Lab F-38000 Grenoble France

This paper considers the issue of state and parameter estimation in an overland flow model, including unknown infiltration coefficients. The overland flow dynamics are described by the continuity Saint-Venant equation, and the infiltration by the Green-Ampt model. Due to the related so-called ponding time, the overall model results in a switched partial differential equation, coupled with an ordinary differential one. In this model, the initial state, the friction coefficient and the infiltration parameters are assumed to be unknown, with only a discrete number of measurements being available. For the estimation, the model is modified by adding an activation function, and then used in the minimization of a cost function defined as the difference between available measurements and the corresponding simulated ones. The variational analysis is applied on the augmented Lagrangian objective functional in order to get the weak form of gradients of this function with respect to the variables to be estimated. Based on these gradients a quasi Newton method is used to solve the optimization problem. An illustration example is finally provided to validate the proposed approach. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Switched Systems Switched overland flow model Green - Ampt model State estimation distributed parameter estimation Switched hyperbolic systems Adjoint method Inverse problem Saint-Venant model

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distributed parameter estimation in unreliable WSNs: Quantized communication and asynchronous intermittent observation

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INFORMATION SCIENCES 2015年 309卷 11-25页

作者： Wang, Huiwei Liao, Xiaofeng Huang, Tingwen Chen, Guo Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Texas A&M Univ Qatar Doha 5825 Qatar Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia

Motivated by the application of wireless sensor networks, we study a class of distributed learning algorithms for estimating an unknown deterministic parameter. In such networks, each sensor activates in an unreliable network environment which admits new nodes joining and old nodes leaving the network, and can tolerate link failures and noisy interference. Additionally, sensors operate under limited computational, bandwidth and energy resources. In order to overcome these constraints, we propose a novel algorithm to reliably estimate the unknown parameter associated with a linear measurement function based on its noisy samples occasionally made at local sensors. Each local sensor processes its observation, updates its estimate through local information exchange and sends its quantized estimate to its one-hop neighbors. Based on the theory of matrix decomposition and stochastic approximation, we show that the studied algorithm converges almost surely to the unknown parameter with probability one, and the sensor estimate is, in expectation, equal to the unknown parameter. Specifically, we further provide the upper bounds on the mean square error and epsilon-convergence time. Finally, we present a numerical example to assess and compare the communication cost with several similar scheme algorithms to achieve a given performance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Asynchronous intermittent observation distributed parameter estimation Quantized communication Unreliable network environment

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distributed parameter estimation in Sensor Networks Based on Stochastic Approximation 34

Distributed Parameter Estimation in Sensor Networks Based on...

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34th Chinese Control Conference (CCC)

作者： Lei, Jinlong Chen, Han-Fu Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China

ISBN: (纸本)9789881563897

A distributed stochastic approximation algorithm with expanding truncations (DSAAWET) is proposed to estimate the unknown parameter linearly appearing in the sensor networks. Each agent updates its local estimate by averaging its neighbors' estimates with weights and by processing its local current observation. The estimates are shown to converge to the true parameter with probability one. A numerical example is given to demonstrate the obtained theoretic result.

关键词： distributed parameter estimation sensor networks stochastic approximation linear observation model

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Bias-compensated based diffusion affine projection like maximum correntropy algorithm

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DIGITAL SIGNAL PROCESSING 2024年 154卷

作者： Li, Chengjin Zhao, Haiquan Xiang, Wang Southwest Jiaotong Univ Sch Elect Engn Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China

A distributed network with highly colored input signal is often located in a scenario where the output signal is subject to impulse noise. Under the above scenario, the performance of the diffusion adaptive algorithms will suffer from performance degradation, which includes distributed parameter estimation accuracy and convergence/tracking rate. To solve these problems, the diffusion affine projection like maximum correntropy (DAPLMC) algorithm is proposed. Moreover, highly colored input signals tend to be mixed with noise, which will lead to biased estimation. To deal with the adverse impact of biased estimation, based on the bias compensation (BC) strategy, the bias-compensated DAPLMC (BC-DAPLMC) algorithm is proposed in this paper. By analyzing the convergence performance of the BC-DAPLMC algorithm, a range of its step size is obtained, and the steadystate error of the BC-DAPLMC algorithm is studied. The superior performance of the BC-DAPLMC algorithm and theoretical steady-state error analysis is validated by simulation results.

关键词： Biased estimation Convergence rate distributed parameter estimation Impulse noise Maximum correntropy criterion (MCC) Tracking rate

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Event-Triggered Proximal Online Gradient Descent Algorithm for parameter estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2024年 72卷 2594-2606页

作者： Zhou, Yaoyao Chen, Gang Chongqing Univ Coll Automat Chongqing 400044 Peoples R China

The constrained composite-convex parameter estimation problem on the networked system, where the composite-convex function consists of a sum of node-specific smooth loss functions and a nonsmooth regularizer, is investigated in this paper. To reduce the communication burden, the event-triggered mechanism is introduced and the novel event-triggered proximal online gradient descent algorithm (EPOGDA) is proposed. The analysis shows that if the event-triggered threshold converges to zero as time tends to infinity and the cumulative difference between consecutive optimal values is sublinear, the dynamic regret of EPOGDA is sublinear. Further, we extend the proposed EPOGDA to the gradient-free scenarios, where the gradients are estimated using the Gaussian smoothed gradient estimator (GSGE). The GSGE-EPOGDA is presented and analyzed, which does not lead to performance degradation as compared to EPOGDA. Finally, the advantages of EPOGDA and GSGE-EPOGDA are verified on a distributed multi-sensor network.

关键词： Heuristic algorithms Signal processing algorithms Sensors estimation Convex functions parameter estimation Dynamic scheduling distributed parameter estimation online convex optimization event-triggered mechanism dynamic regret proximal algorithm

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distributed Adaptive Fuzzy Modeling for Industrial Gas-Electricity Networks With Multioperation Modes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第5期19卷 6647-6658页

作者： Su, Lei Zhao, Jun Wang, Wei Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

Industrial gas-electricity networks typically operate in several modes due to large demand variations. In this article, the nonlinear spatio-temporal transient phenomenon of gas networks (GNs) is analyzed by a novel modeling approach, named Takagi-Sugeno fuzzy linear graph (TSFLG), and then, a parameter space decomposition-based adaptive parameter estimation algorithm is developed for greater efficiency. By merging TS fuzzy theory, the nonlinear terms are replaced by a set of linear elements interpolated by membership functions, which are then connected in series to construct the TSFLG structure. The GN is partitioned into several subnetworks for parallel estimation, and the coupling parameters are determined by the covariance intersection fusion rule. Within each subnetwork, efficiency is further improved by estimating alternately linear and nonlinear parameters. The proposal is assessed in an industrial GN with 12 nodes. Test results indicate that the proposed method improves efficiency by 38.64$\%$ while maintaining accuracy.

关键词： Mathematical models Pipelines parameter estimation Transient analysis Adaptation models Nonlinear dynamical systems Informatics distributed parameter estimation industrial park linear graph multienergy system Takagi-Sugeno (TS) fuzzy theory transient model

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