Finite-Horizon Variance-Constrained Estimation for Complex Networks Subject to Dynamical Bias Using Binary Encoding Schemes

• Published in: IEEE access Vol. 11; pp. 142589 - 142600
• Main Authors: Li, Weijian, Hou, Nan, Yang, Fan, Bu, Xianye, Sun, Ligang
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023; IEEE
• Subjects: Algorithms; Bias; binary encoding schemes; Coding; Constraints; dynamical bias; Nonlinearity; Signal transmission; State estimation; stochastic nonlinearities; Time-varying complex networks; Upper bounds; Variance; variance-constrained state estimation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3341425

In this article, a variance-constrained [Formula Omitted] state estimation issue is dealt with for a type of nonlinear time-varying complex networks affected by dynamical bias under binary encoding schemes (BESs). The BESs are used during signal transmission in view of the security of binary bit strings. The stochastic bias is involved using a dynamical equation, and stochastic nonlinearity is characterized by statistical property. The purpose of this article is to construct a finite-horizon state estimator, such that the estimation error dynamics satisfies performance requirements of both the prescribed upper bound constraint on the error variance and the [Formula Omitted] noise rejection. By employing the matrix inequality approach and random analysis, sufficient conditions are established for the presence of the state estimator. Subsequently, the gain parameters of the constructed estimator are acquired by solving some recursive matrix inequalities. Ultimately, the correctness of the developed estimation algorithm is testified via a numerical simulation example.