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distributed consensus filter FOR A CLASS OF CONTINUOUS-TIME NONLINEAR STOCHASTIC SYSTEMS IN SENSOR NETWORKS

ASIAN JOURNAL OF CONTROL 2017年第4期19卷 1284-1294页

作者： Jenabzadeh, Ahmadreza Safarinejadian, Behrouz Mohammadnia, Foroogh Shiraz Univ Technol Sch Elect & Elect Engn Shiraz Iran

This paper investigates the state estimation problem for continuous-time nonlinear stochastic systems in sensor networks. For this purpose, a distributed consensus filter (DCF) is proposed based on Lyapunov stability theory for every node in a sensor network. It will be proved that the mean square of the estimation error is exponentially ultimately bounded. This filter can estimate the states of nonlinear stochastic systems, the nonlinear functions of which satisfy a pseudo Lipschitz condition. Sufficient conditions for the existence of this filter are sensor network connectivity and LMI solvability of DCF. Furthermore, a criterion is presented to optimize the filter gain based on minimizing the upper consensus bound of estimation error. Simulation results show the promising performance of the proposed filter.

关键词： distributed consensus filter Nonlinear stochastic systems Pseudo Lipschitz condition Sensor networks State estimation

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Data-driven distributed consensus filter for a Discrete-time Nonlinear Sensor Network 9

Data-driven Distributed Consensus Filter for a Discrete-time...

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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Bai, Chuandong Ji, Honghai Wei, Yuzhou Pang, Zhonghua Hou, Zhongsheng North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

ISBN: (纸本)9781728159225

In this work, a novel data-driven distributed consensus filter (DD-DCF) is proposed based on the dynamic linearization technique (DLT) for a discrete-time nonlinear sensor network. Compared with conventional model-based consensus filters, the proposed method is data-driven merely depending on the input and output (I/O) data from measurements. Both the data-driven system identification (DD-SI) algorithm and the distributed consensus filter state estimation (DCF-SE) algorithm are investigated for a nonlinear sensor network. The theoretical analysis shows the main result of the DD-DCF algorithm in the sensor network. The simulation results verify the effectiveness of the designed approach.

关键词： Dynamic Linearization Technique System Identification State Estimation distributed consensus filter Sensor Network

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distributed consensus filtering for jump Markov linear systems

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IET CONTROL THEORY AND APPLICATIONS 2013年第12期7卷 1659-1664页

作者： Li, Wenling Jia, Yingmin Du, Junping Zhang, Jun Beihang Univ BUAA Res Div 7 Beijing 100191 Peoples R China Beihang Univ BUAA Dept Syst & Control Beijing 100191 Peoples R China Beihang Univ BUAA Key Lab Math Informat & Behav Semant LMIB SMSS Beijing 100191 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci & Technol Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Beihang Univ BUAA Sch Elect & Informat Engn Beijing 100191 Peoples R China

This article studies the problem of distributed filtering for jump Markov linear systems in a not fully connected sensor network. A distributed consensus filter is developed by applying an improved interacting multiple model approach in which the mode-conditioned estimates are derived by the Kalman consensus filter and the mode probabilities are obtained in the sense of linear minimum variance. A numerical example is provided to demonstrate the effectiveness of the proposed algorithm for tracking a manoeuvring target in a sensor work with eight nodes.

关键词： estimation theory Kalman filters linear systems Markov processes probability target tracking jump Markov linear system distributed consensus filter model approach mode conditioned estimation Kalman consensus filter linear minimum variance mode probability manoeuvring target tracking

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distributed consensus filtering in Sensor Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2009年第6期39卷 1568-1577页

作者： Yu, Wenwu Chen, Guanrong Wang, Zidong Yang, Wen City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong Peoples R China Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England E China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

In this paper, a new filtering problem for sensor networks is investigated. A new type of distributed consensus filters is designed, where each sensor can communicate with the neighboring sensors, and filtering can be performed in a distributed way. In the pinning control approach, only a small fraction of sensors need to measure the target information, with which the whole network can be controlled. Furthermore, pinning observers are designed in the case that the sensor can only observe partial target information. Simulation results are given to verify the designed distributed consensus filters.

关键词： consensus distributed consensus filter pinning control pinning observer sensor network

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distributed consensus Student-t filter for Sensor Networks With Heavy-Tailed Process and Measurement Noises

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IEEE ACCESS 2020年 8卷 167865-167874页

作者： Wang, Jinran Dong, Peng Shen, Kai Song, Xun Wang, Xiaodong Beijing Inst Elect Syst Engn State Key Lab Intelligent Mfg Syst Technol Beijing 100854 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut Shanghai 200240 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

In the estimation of distributed sensor networks, process noise and measurement noise may have outliers which have heavy-tailed characteristics. To solve this problem, this paper proposes a distributed consensus estimating method for sensor networks based on Student-t distribution. In the state space model, both process noise and measurement noise are modeled as Student-t distributions with heavy-tailed characteristics. First, for the assumption that the process noise and measurement noise have the same degree of freedom parameters, an exact distributed consensus Student-t filtering algorithm is derived. In practical applications, this assumption is often not true, and due to the increasing degrees of freedom, the method will quickly converge to the traditional distributed consensus Kalman filter. Therefore, it is necessary to relax the assumption of the same degree of freedom and keep the degree of freedom unchanged within a certain range. Based on this, an approximate distributed consensus Student-t filter algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.

关键词： Student-t distribution distributed consensus filter distributed sensor networks

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distributed fault-tolerance consensus filtering in wireless sensor networks-Part I: communication failure

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2016年第2期22卷 127-142页

作者： He, Ping Fan, Tao Sichuan Univ Sci & Engn Sch Automat & Elect Informat Dept Automat Zigong 643000 Sichuan Peoples R China Sichuan Univ Sci & Engn Artificial Intelligence Key Lab Sichuan Prov 180 Xueyuan StHuixing Rd Zigong 643000 Sichuan Peoples R China

In this paper, a distributed fault-tolerance consensus filtering problem for wireless sensor networks (WSNs) with communication failure is investigated. A new type of distributed faulttolerance consensus filter is designed, where each sensor can communicate with the neighbouring sensors, and filtering can be performed in a distributed coordinated function. Because the sensor networks may be disturbed by loss of transmitting effectiveness, the performance of sensor network will be degraded by using the faulted signals. Under the unknown assumption for the failures, adaptive laws are proposed to estimate the failures factors. Then based on the information of adaptive schemes, a distributed fault-tolerance consensus filter scheme is designed to guarantee that all sensors asymptotically trace the target in the presence of the uncertain faults. Simulation results are given to verify the feasibility and effectiveness of the designed distributed fault-tolerance consensus filter.

关键词： fault-tolerance distributed consensus filter WSN wireless sensor network adaptive control coordination control communication failure

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distributed Soft-Data-Constrained Multi-Model Particle filter

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第3期45卷 384-394页

作者： Seifzadeh, Sepideh Khaleghi, Bahador Karray, Fakhri Univ Waterloo Ctr Pattern Anal & Machine Intelligence Waterloo ON N2L 3G1 Canada

A distributed nonlinear estimation method based on soft-data-constrained multimodel particle filtering and applicable to a number of distributed state estimation problems is proposed. This method needs only local data exchange among neighboring sensor nodes and thus provides enhanced reliability, scalability, and ease of deployment. To make the multimodel particle filtering work in a distributed manner, a Gaussian approximation of the particle cloud obtained at each sensor node and a consensus propagation-based distributed data aggregation scheme are used to dynamically reweight the particles' weights. The proposed method can recover from failure situations and is robust to noise, since it keeps the same population of particles and uses the aggregated global Gaussian to infer constraints. The constraints are enforced by adjusting particles' weights and assigning a higher mass to those closer to the global estimate represented by the nodes in the entire sensor network after each communication step. Each sensor node experiences gradual change;i.e., if a noise occurs in the system, the node, its neighbors, and consequently the overall network are less affected than with other approaches, and thus recover faster. The efficiency of the proposed method is verified through extensive simulations for a target tracking system which can process both soft and hard data in sensor networks.

关键词： Constraint filtering distributed consensus filter distributed filtering fuzzy logic multiple model particle filter sensor fusion sensor network soft data target tracking

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distributed Multiple Human Tracking with Wireless Binary Pyroelectric Infrared (PIR) Sensor Networks

Distributed Multiple Human Tracking with Wireless Binary Pyr...

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2010 IEEE Sensors Conference

作者： Hao, Qi Hu, Fei Lu, Jiang Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA

ISBN: (纸本)9781424481682

This paper presents a distributed multiple human tracking system based on binary pyroelectric infrared (PIR) sensors. The goal of our research is to make wireless distributed pyroelectric sensors a low-cost, low-data-throughput alternative to the expensive infrared video sensors in surveillance applications. With the help of coded Fresnel lens arrays, a binary pyroelectric sensor array can measure the angular displacements of up to two thermal targets. The distributed multiple target tracking scheme is achieved by using (1) joint probabilistic association and (2) consensus filtering. The former can facilitate each sensor node to fuse the measurements and states of nodes within its neighborhood. The latter can guarantee that a consensus will be achieved among those distributed sensor nodes. A prototype wireless pyroelectric sensor network system has been developed to demonstrate the scalability and performance of the proposed distributed multiple human tracking system.

关键词： Joint probabilistic association distributed consensus filter binary pyroelectric sensor

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distributed state estimation with state equality constraints in the presence of packet dropping

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APPLIED MATHEMATICS AND COMPUTATION 2023年第1期451卷

作者： Fu, Xiaoyu Song, Xinmin Liu, Xiyu Zhang, Min Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Shandong Normal Univ Sch Business Jinan 250358 Peoples R China

In this paper, we investigate distributed Kalman consensus filter with state equality con-straints in the presence of packet dropping. Firstly, the unconstrained distributed Kalman filter is designed by utilizing the local measurement information of each sensor node, and then the consensus term is added to it to derive the unconstrained distributed Kalman consensus filter. Secondly, we research the equality constrained distributed Kalman con-sensus filter based on the projection operator to achieve better filter performance, where the unconstrained estimation gain is calculated by solving a modified algebraic Riccati equation and a Lyapunov equation. Additionally, we design the second type of equality constrained distributed Kalman consensus filter by using time-stamping technology and projection operator to eliminate the effect of the Lyapunov equation in the infinite time domain on the convergence analysis, and its unconstrained estimation gain only requires solving a modified algebraic Riccati equation. Finally, the simulation experiments demon-strate that the equality constrained distributed Kalman consensus filter outperforms the unconstrained filter and that the second type of estimator exhibits superior performance compared to the first.(c) 2023 Elsevier Inc. All rights reserved.

关键词： distributed Kalman filtering Packet dropping distributed consensus filter State equality constraints

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