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Recursive distributed filtering for two-dimensional shift-varying systems over sensor networks under stochastic communication protocols

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AUTOMATICA 2020年第0期115卷 108865-000页

作者： Wang, Fan Wang, Zidong Liang, Jinling Liu, Xiaohui Southeast Univ Sch Automat Nanjing 210096 Peoples R China Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middx England Southeast Univ Sch Math Nanjing 210096 Peoples R China

This paper is concerned with the distributed filtering problem for a class of two-dimensional shift-varying systems over sensor networks subject to stochastic communication protocol (SCP) over a finite horizon. The communication between the sensor nodes and the filters is implemented through shared channels of limited capacity. To avoid data collisions, the SCP is applied to determine the transmission order of the signals/packets for each sensor. The considered scheduling behavior is characterized by mutually uncorrelated random variables with known probability distributions. Recursive distributed filters are proposed to estimate the system state through available information from both individual and neighboring nodes in the sensor network according to a given topology. Attention is focused on the design of distributed filters in order to ensure the locally minimal upper bound on the error variance of the state estimation. Sufficient conditions are first established, via intensive stochastic analysis and mathematical induction, on the existence of an upper bound of the estimation error variance. Then, by means of a matrix simplification technique, the desired filter gains are designed to optimize the obtained upper bound at each shift step. Finally, a practical example is given to verify the effectiveness of the proposed filter strategy. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Sensor networks Two-dimensional systems distributed filtering Stochastic communication protocol Shift-varying systems Recursive approach

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A distributed motion planning and distributed filtering approach for target tracking in mobile sensor networks

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IFAC Proceedings Volumes 2011年第1期44卷 4771-4778页

作者： Gerasimos G. Rigatos Unit of Industrial Automation Industrial Systems Institute 26504 Rion Patras Greece

Abstract The paper studies the problem of tracking of a target by a multi-robot system assuming that the target's state vector is not directly measurable and has to be estimated by distributed filtering based on the target's cartesian coordinates and bearing measurements obtained by the individual mobile robots. The objective is to make the robots converge in a synchronized manner towards the target, while avoiding collisions between them and avoiding collisions with obstacles in the motion plane. To solve the overall problem, the following steps are followed: (i) distributed filtering, so as to obtain an accurate estimation of the target's state vector. This estimate provides the desirable state vector to be tracked by each one of the mobile robots, (ii) motion planning and control that enables convergence of the vehicles to the goal position and also maintains the cohesion of the vehicles swarm. The efficiency of the proposed distributed filtering and distributed motion planning scheme is tested through simulation experiments.

关键词： target tracking multi-robot system mobile sensor networks distributed motion planning distributed filtering

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Derivative-Free distributed filtering for MIMO Robotic Systems Under Delays and Packet Drops

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2013年第2期10卷

作者： Rigatos, Gerasimos G. Ind Syst Inst Unit Ind Automat Rion Greece

This paper presents an approach to distributed state estimation-based control of nonlinear MIMO systems, capable of incorporating delayed measurements in the estimation algorithm while also being robust to packet losses. First, the paper examines the problem of distributed nonlinear filtering over a communication/sensors network, and the use of the estimated state vector in a control loop. As a possible filtering approach, an extended information filter (EIF) is proposed. The extended information filter requires the computation of Jacobians which in the case of high order nonlinear dynamical systems can be a cumbersome procedure, while it also introduces cumulative errors to the state estimation due to the approximative linearization performed in the Taylor series expansion of the system's nonlinear model. To overcome the aforementioned weaknesses of the extended information filter, a derivative-free approach to extended information filtering has been proposed. distributed filtering is now based on a derivative-free implementation of Kalman filtering which is shown to be applicable to MIMO nonlinear dynamical systems. In the proposed derivative-free extended information filtering, the system is first subject to a linearization transformation that makes use of the differential flatness theory. It is shown how the proposed distributed filtering method can succeed in compensation of random delays and packet drops which may appear during the transmission of measurements and of state vector estimates, thus assuring a reliable performance of the distributed filtering-based control scheme. Evaluation tests are carried out on benchmark MIMO nonlinear systems, such as multi-DOF robotic manipulators.

关键词： distributed filtering Derivative-Free Nonlinear Kalman filtering State Estimation-Based Control Nonlinear MIMO Dynamical Systems Robotic Manipulators Networked Control Systems Communication Delays Packet Drops

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A Two-Phase distributed filtering Algorithm for Networked Uncertain Systems with Fading Measurements under Deception Attacks

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SENSORS 2020年第22期20卷 6445.-6445.页

作者： Caballero-Aguila, Raquel Hermoso-Carazo, Aurora Linares-Perez, Josefa Univ Jaen Dept Estadist Jaen 23071 Spain Univ Granada Dept Estadist Avda Fuentenueva Granada 18071 Spain

In this paper, the distributed filtering problem is addressed for a class of discrete-time stochastic systems over a sensor network with a given topology, susceptible to suffering deception attacks, launched by potential adversaries, which can randomly succeed or not with a known success probability, which is not necessarily the same for the different sensors. The system model integrates some random imperfections and features that are frequently found in real networked environments, namely: (1) fading measurements;(2) multiplicative noises in both the state and measurement equations;and (3) sensor additive noises cross-correlated with each other and with the process noise. According to the network communication scheme, besides its own local measurements, each sensor receives the measured outputs from its adjacent nodes. Based on such measurements, a recursive algorithm is designed to obtain the least-squares linear filter of the state. Thereafter, each sensor receives the filtering estimators previously obtained by its adjacent nodes, and these estimators are all fused to obtain the desired distributed filter as the minimum mean squared error matrix-weighted linear combination of them. The theoretical results are illustrated by a simulation example, where the efficiency of the developed distributed estimation strategy is discussed in terms of the error variances.

关键词： networked uncertain systems fading measurements distributed filtering random deception attacks

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distributed Resilient filtering for Power Systems Subject to Denial-of-Service Attacks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2019年第8期49卷 1688-1697页

作者： Chen, Wei Ding, Derui Dong, Hongli Wei, Guoliang Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Northeast Petr Univ Inst Complex Syst & Adv Control Daqing 163318 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligen Daqing 163318 Peoples R China Univ Shanghai Sci & Technol Sch Sci Shanghai 200093 Peoples R China

This paper addresses the distributed resilient filtering problem for a class of power systems subject to denial-ofservice (DoS) attacks. A novel distributed filter is first constructed to practically reflect the impact from both cyber-attacks and gain perturbations. For all possible occurrence of DoS attacks and gain perturbations, an upper bound of filtering error covariance is derived by resorting to some typical matrix inequalities. Furthermore, the desired filter gain relying on the solution of two Riccati-like difference equations is obtained with the help of the gradient-based approach and the mathematical induction. The developed algorithm with a recursive form is independent of the global information and thus satisfies the requirements of scalability and distributed implementation online. Finally, a benchmark simulation test is exploited to check the usefulness of the designed filter.

关键词： Denial-of-service (DoS) attack distributed filtering gain perturbations power systems resilient filter

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distributed recursive filtering under random access protocols: A multirate strategy

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第12期32卷 7132-7148页

作者： Liu, Shuai Zhao, Xiaoxiao Tian, Engang Wei, Guoliang Univ Shanghai Sci & Technol Coll Sci Shanghai Peoples R China Qingdao Univ Sci & Technol Coll Automat Elect Engn Qingdao Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Business Sch Shanghai Peoples R China

This article addresses the distributed recursive filtering problem for a class of time-varying multirate systems in sensor networks with randomly occurring nonlinearities and the communication protocol. A multirate model is considered where the sampling period of the sensors is slower than the updating period of the system state. Accordingly, the lifting technique is proposed to transform the multirate system into a single-rate system. In order to avoid data collisions, the random access protocol (RAP) scheduling is applied to determine which signal/packet selected by RAP scheduling can access the shared network for each sensor. Attention is focused on the design of a distributed recursive filter such that, in the simultaneous presence of randomly occurring nonlinearities, the multirate strategy and the RAP scheduling, an upper bound for the filtering error covariance is obtained with the help of the mathematical induction method. Furthermore, by utilizing a novel matrix simplification technique, the filter parameters are recursively calculated by minimizing the obtained upper bound. Finally, the effectiveness of the proposed distributed recursive filtering scheme is demonstrated via a numerical example.

关键词： distributed filtering multirate systems random access protocol recursive filtering sensor networks

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distributed H_∞ filtering for a Class of Markovian Jump Nonlinear Time-Delay Systems Over Lossy Sensor Networks

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2013年第10期60卷 4665-4672页

作者： Dong, Hongli Wang, Zidong Gao, Huijun Northeast Petr Univ Coll Elect & Informat Engn Daqing 163318 Peoples R China Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150001 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England

This paper is concerned with the distributed H-infinity filtering problem for a class of discrete-time Markovian jump nonlinear time-delay systems with deficient statistics of mode transitions. The system measurements are collected through a lossy sensor network subject to randomly occurring quantization errors and randomly occurring packet dropouts. The description of deficient statistics of mode transitions that account for known, unknown, and uncertain transition probabilities is comprehensive. A distributed filter design scheme is outlined by explicitly characterizing the filter gains in terms of some matrix inequalities. Simulation results demonstrate the effectiveness of the proposed filtering scheme.

关键词： Deficient statistics of mode transitions distributed filtering lossy sensor networks packet losses quantization errors

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distributed entropy filtering subject to DoS attacks in non-Gauss environments

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2020年第3期30卷 1240-1257页

作者： Song, Haifang Ding, Derui Dong, Hongli Wei, Guoliang Han, Qing-Long Univ Shanghai Sci & Technol Coll Sci Shanghai Peoples R China Univ Shanghai Sci & Technol Dept Control Sci & Engn Shanghai 200093 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia Northeast Petr Univ Inst Complex Syst & Adv Control Daqing Peoples R China

This paper is concerned with distributed entropy filtering for a class of time-varying systems subject to non-Gaussian noises and denial-of-service attacks. A distributed Kalman-type filter is constructed by fusing the information from neighboring sensors in the one-step prediction estimation. The maximum correlation entropy criterion with weighted Gaussian kernels instead of the traditional least squares and minimum variance indices is then employed to evaluate the filtering performance under the more general case of non-Gaussian noises. For the considered scenario, a new algorithm of distributed maximum correntropy criterion Kalman filters is developed by utilizing traditional fixed-point iterative rules, and the desired filter gains are dependent on both the one-step prediction cross-variance and the weighted Gaussian kernel function. In light of a similar analysis process and an introduced auxiliary cost, its suboptimal version is proposed to avoid the calculation of cross-variance and thereby satisfying the requirement of real time. Furthermore, a degenerated result is also derived under the traditional correntropy criterion (ie, with unit weighted matrices). Finally, the simulation results show the merit of proposed distributed maximum correntropy criterion Kalman filters in the presence of denial-of-service attacks and non-Gaussian noises.

关键词： denial of service attacks distributed filtering fixed-point iterative rule maximum correntropy criterion non-Gaussian noises

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Consensus-Based distributed Robust filtering for Multisensor Systems With Stochastic Uncertainties

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IEEE SENSORS JOURNAL 2018年第18期18卷 7611-7618页

作者： Rastgar, Fatemeh Rahmani, Mehdi Imam Khomeini Int Univ Dept Elect Engn Qazvin *** Iran

This paper proposes a distributed robust Kalman filter for time-varying uncertain linear multisensor systems subjected to stochastic uncertainties. A consensus algorithm is utilized to compromise on a single data among information of all nodes in a multisensor system. distributed filtering based on consensus remarkably stands out from other algorithms because each node not only estimates its local states correctly, but also reaches an agreement with other nodes over a sensor network as well. The distributed filtering problem is formulated here using consensus on estimation (CE) algorithm. The optimal parameters of the filter are computed by minimizing the covariance of the estimation error. Moreover, the stability of the touched upon algorithm is proved by the Lyapunov stability theorem. Simulation results fora multisensor system with 100 nodes are presented to show the effectiveness and performance of the proposed CE-based distributed robust filtering approach.

关键词： Multisensor systems distributed filtering consensus on estimation stochastic uncertainty

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distributed Recursive filtering Over Sensor Networks With Nonlogarithmic Sensor Resolution

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第10期67卷 5408-5415页

作者： Chen, Hongwei Wang, Zidong Shen, Bo Liang, Jinling Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai 201620 Peoples R China Brunel Univ London Dept Comp Sci London UB8 3PH England Southeast Univ Sch Math Nanjing 210096 Peoples R China

Sensor resolution, which is one of the most important parameters/specifications for almost all kinds of sensors, plays an important role in any signal processing problems. This article deals with the distributed filtering problem for a class of discrete time-varying stochastic systems subject to nonlogarithmic sensor resolution and stochastic nonlinearities. The soft measurement technique is exploited in the filter design to overcome the difficulties resulting from the sensor-resolution-induced (SRI) uncertainty. The aim of the presented filtering problem is to construct the distributed filter over a sensor network such that in the presence of SRI uncertainty and stochastic nonlinearity, an upper bound on the filtering error covariance is guaranteed and subsequently minimized by appropriately designing the filer parameters at each time instant. Moreover, a matrix simplification method is utilized to tackle the difficulties stemming from the sparsity of sensor networks. Finally, a numerical example is employed to illustrate the effectiveness of the proposed filtering scheme.

关键词： Wireless sensor networks Target tracking Stochastic processes Measurement uncertainty Data models Signal resolution Kalman filters distributed filtering recursive filtering sensor resolution (SR) stochastic nonlinearity wireless sensor networks (WSNs)

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