Variance-constrained filtering for nonlinear systems with randomly occurring quantized measurements: recursive scheme and boundedness analysis

Abstract

In this paper, the robust optimal filtering problem is discussed for time-varying networked systems with randomly occurring quantized measurements via the variance-constrained method. The stochastic nonlinearity is considered by statistical form. The randomly occurring quantized measurements are expressed by a set of Bernoulli distributed random variables, where the quantized measurements are described by the logarithmic quantizer. The objective of this paper is to design a recursive optimal filter such that, for all randomly occurring uncertainties, randomly occurring quantized measurements and stochastic nonlinearity, an optimized upper bound of the estimation error covariance is given and the desired filter gain is proposed. In addition, the boundedness analysis problem is studied, where a sufficient condition is given to ensure the exponential boundedness of the filtering error in the mean-square sense. Finally, simulations with comparisons are proposed to demonstrate the validity of the presented robust variance-constrained filtering strategy.

Variance-constrained resilient $H_{\infty }$ state estimation for time-varying neural networks with randomly varying nonlinearities and missing measurements

“…During the network communications or transmissions, the perfect measurements can not be always available, thus increasing research effort has been made on the state estimation problems against the missing measurements [20][21][22]. The missing measurements are inevitable primarily due to the signal interference during the transmission including limited communication channels, noise interferences and so forth.…”