The work proposes a distributedkalmanfiltering (KF) algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way. We provide the stability anal...
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The work proposes a distributedkalmanfiltering (KF) algorithm to track a time-varying unknown signal process for a stochastic regression model over network systems in a cooperative way. We provide the stability analysis of the proposed distributed KF algorithm without independent and stationary signal assumptions, which implies that the theoretical results are able to be applied to stochastic feedback systems. Note that the main difficulty of stability analysis lies in analyzing the properties of the product of non-independent and non-stationary random matrices involved in the error equation. We employ analysis techniques such as stochastic Lyapunov function, stability theory of stochastic systems, and algebraic graph theory to deal with the above issue. The stochastic spatio-temporal cooperative information condition shows the cooperative property of multiple sensors that even though any local sensor cannot track the time-varying unknown signal, the distributed KF algorithm can be utilized to finish the filtering task in a cooperative way. At last, we illustrate the property of the proposed distributed KF algorithm by a simulation example.
The distributed estimation problem for wireless sensor networks with limited communication/sensing ranges and observability is studied. A novel sensor measuring activation scheme based on a fully distributed event-tri...
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The distributed estimation problem for wireless sensor networks with limited communication/sensing ranges and observability is studied. A novel sensor measuring activation scheme based on a fully distributed event-triggered strategy is proposed to make each node achieve a better trade-off between estimation error and energy saving. The strategy depends on both the predicted synthetic performance index and the predicted position of the target. A distributed kalman filtering algorithm based on the minimum trace fusion principle is proposed. It is proved that comparing with the time-triggered strategy, the proposed event-triggered measuring strategy has better performance. Although the event-triggered measuring topology is time-varying and each sensor is not observable, it is proved that as long as there exists at least one collaboratively observable sensor in the available distance-based sensing network at each time instant, the estimation errors are bounded in mean square sense. Simulation examples are given to illustrate the validity of the algorithm.
distributed sensor networks have been widely employed to monitor and protect critical infrastructure assets. The network status can be estimated by centralized state estimation using coordinated data aggregation or by...
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ISBN:
(纸本)9781424456383
distributed sensor networks have been widely employed to monitor and protect critical infrastructure assets. The network status can be estimated by centralized state estimation using coordinated data aggregation or by distributed state estimation, where nodes only exchange information locally to achieve enhanced scalability and adaptivity to network dynamics. One important property of state estimation is robustness against false data injection from sensors compromised by attackers. Different from most existing works in the literature that focus on centralized state estimation, we propose two novel robust distributed state estimation algorithms against false data injection. They are built upon an existing distributed kalman filtering algorithm. In the first algorithm, we use variational Bayesian learning to estimate attack parameters and achieve performance similar to a centralized majority voting rule, without causing extra communication overhead. In the second algorithm, we introduce heterogeneity into the network by utilizing a subset of pre-trusted nodes to achieve performance better than majority voting. We show that as long as there is a path connecting each node to some of the pre-trusted nodes, the attackers can not subvert the network. Experimental results demonstrate the effectiveness of our proposed schemes.
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