Deconvolution filtering for nonlinear stochastic systems with randomly occurring sensor delays via probability-dependent method

Abstract

This paper deals with a robust H inf deconvolution filtering problem for discrete-time nonlinear stochastic systems with randomly occurring sensor delays. The delayed measurements are assumed to occur in a random way characterized by a random variable sequence following the Bernoulli distribution with time-varying probability.The purpose is to design an Hinf deconvolution filter such that, for all the admissible randomly occurring sensor delays, nonlinear disturbances, and external noises, the input signal distorted by the transmission channel could be recovered to a specified extent. By utilizing the constructed Lyapunov functional relying on the time-varying probability parameters, the desired sufficient criteria are derived.The proposed Hinf deconvolution filter parameters include not only the fixed gains obtained by solving a convex optimization problem but also the online measurable timevarying probability.When the time-varying sensor delays occur randomly with a time-varying probability sequence, the proposed gain-scheduled filtering algorithm is very effective. The obtained design algorithm is finally verified in the light of simulation examples.