A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements
By Jun Hu, Zidong Wang, Steven Liu, Huijun Gao. at - Automatisierungstechnik, 2016
376 citations recorded by the source corpus.
In this paper, the recursive state estimation problem is investigated for an array of discrete time-varying coupled stochastic complex networks with missing measurements. A set of random variables satisfying certain probabilistic distributions is introduced to characterize the phenomenon of the missing measurements, where each sensor can have individual missing probability. The Taylor series expansion is employed to deal with the nonlinearities and the high-order terms of the linearization errors are estimated. The purpose of the addressed state estimation problem is to design a time-varying state estimator such that, in the presence of the missing measurements and the random disturbances, an upper bound of the estimation error covariance can be guaranteed and the explicit expression of the estimator parameters is given. By using the Riccati-like difference equations approach, the estimator parameter is characterized by the solutions to two Riccati-like difference equations. It is shown that the obtained upper bound is minimized by the designed estimator parameters and the proposed state estimation algorithm is of a recursive form suitable for online computation. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the developed state estimation scheme.
Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/cadfa704363feda3b683ca9f4a300ec1332523e5
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In Artificial Intelligence
In Computer Networks and Communications
In Control and Systems Engineering
- Qing-Long Han
- Derui Ding
- Zidong Wang
- Xiaohui Liu
- Yurong Liu
- Guoliang Wei
- Xianming Zhang
- Fuad E. Alsaadi
Citations in the corpus
- A survey of deep neural network architectures and their applications (2017, background): “It should be pointed out that, so far, there have been a multitude of resea ch results on the stability analysis, stabilization and synchronization problems for various types of systems a nd networks in the literature, see [64], [67], [68], [102], [143], [163], [168], [174] for some recent publicati”
- Security Control for Discrete-Time Stochastic Nonlinear Systems Subject to Deception Attacks (2018, background): “sion delays, [14], [32] for systems with missing measurements, [16], [21] for systems with signal quantization, and [10], [11] for systems with randomly occurring uncer-”