Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11957
Title: A variance-constrained approach to recursive state estimation for time-varying complex networks with missing measurements
Authors: Hu, J
Wang, Z
Liu, S
Gao, H
Keywords: State estimation;Time-varying complex networks;Variance constraints;Missing measurements;Recursive approach;Riccati-like difference equations
Issue Date: 2016
Publisher: Elsevier
Citation: Automatica, 64, pp. 155 - 162, (2016)
Abstract: In this paper, the recursive state estimation problem is investigated for an array of discrete timevarying 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.
URI: http://www.sciencedirect.com/science/article/pii/S0005109815004720
http://bura.brunel.ac.uk/handle/2438/11957
DOI: http://dx.doi.org/10.1016/j.automatica.2015.11.008
ISSN: C
C
0005-1098
Appears in Collections:Dept of Computer Science Research Papers

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