Resolution-Dependent State Estimation for a Class of Nonlinear Coupled Complex Networks With Stochastic Communication and Correlated Noises

This article proposes the design of the resolution-dependent variance-constrained state estimation (RDVCSE) algorithm for a class of time-varying nonlinear coupled complex networks (TVNCCNs) with stochastic communication and correlated noises. Specifically, a continuous-differentiable nonlinear function with bounded first partial derivative is considered during the exchange among different coupled units and a resolution-limited model is taken into account to embody the limited data-processing capabilities of sensors. In order to describe the principle of random allocation in engineering, a stochastic strategy is employed in the sensor/estimator shared channel. An augmented RDVCSE method is developed such that the error covariance upper bound of state estimation (ECUBSE) can be guaranteed and obtained first. Then, the estimator parameter can be concretized via optimizing the trace of ECUBSE. In addition, a sufficient criterion is provided to verify the uniform boundedness of the presented RDVCSE algorithm. Finally, a comparative simulation is carried out to illustrate the validity of the introduced RDVCSE algorithm.