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4. Distributed Dimensionality Reduction Fusion Estimation for Stochastic Uncertain Systems With Fading Measurements Subject to Mixed Attacks.

Author

Fan, Sha, Yan, Huaicheng, Zhang, Hao, Wang, Yueying, Peng, Yan, and Xie, Shaorong

Subjects
*UNCERTAIN systems, *STOCHASTIC systems, *RANDOM variables, *DENIAL of service attacks, *COVARIANCE matrices, *STOCHASTIC processes
Abstract

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Published
2022
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6. Dynamic Event-Based Non-Fragile Dissipative State Estimation for Quantized Complex Networks With Fading Measurements and Its Application.

Author

Fan, Sha, Yan, Huaicheng, Zhang, Hao, Shen, Hao, and Shi, Kaibo

Subjects
*STOCHASTIC analysis, *MARKOV processes, *COEFFICIENTS (Statistics), *DATA packeting, *MEASUREMENT, *MEASUREMENT errors
Abstract

This article is concerned with the issue of dynamic event-based non-fragile dissipative state estimation for a type of stochastic complex networks (CNs) subject to a randomly varying coupling as well as fading measurements, where the variation of coupling is governed by a Markov chain. To characterize the measurement fading phenomenon for different nodes, a Rice fading model is considered with known statistics information of the coefficients. For the sake of further resource saving, a dynamic event-triggering strategy (ETS), which is proved to release less data packets than the static one, is implemented to govern the measurements transmission for each sensor to its corresponding estimator. The main objective of this article is to determine a dynamic event-based non-fragile estimator such that, for all possible parameter fluctuations in estimator gains, the estimation error system is stochastically stable with a strict ($\Upsilon _{1}, \Upsilon _{2}, \Upsilon _{3}$)- $\gamma $ -dissipativity. Through intensive stochastic analysis, sufficient conditions are then derived in terms LMI to guarantee the existence of the desired state estimator. Finally, the effectiveness of the proposed results are verified by two practical examples of Chua’s circuit and quadruple-tank process system (QTPS). [ABSTRACT FROM AUTHOR]

Published
2021
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8. Design of distributed consensus kalman filter based on energy optimization

Author

Sun Chao, Fan Sha, Wang Hua, and Yang Chunxi

Subjects
Random graph, Moving horizon estimation, Extended Kalman filter, Consensus, Computer science, Percolation, Distributed computing, Node (networking), Fast Kalman filter, Kalman filter, Energy consumption, Wireless sensor network, Algorithm
Abstract

As to the problem of limited node power in wireless sensor networks, a new energy efficient distributed kalman filtering algorithm is proposed based on distributed consensus kalman filtering algorithm given by Olfati-Saber and site percolation model- a kind of random graph. In this algorithm, each cluster contains with a cluster-head and some member nodes where the cluster-head is used to fuse data which come from member nodes and consensus process between neighbor cluster-head. Then, the convergence of this new algorithm is proved by applying to Lyapunov second method and matrix theory. At last, a simulation example is given to show that compared with the Saber's algorithm, the algorithm proposed in this paper not only has the approximate estimation performance, but also decrease node energy consumption effectively, and then greatly prolongs the life of wireless sensor networks.

Published
2014

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