A study on input noise second-order filtering and smoothing of linear stochastic discrete systems with packet dropouts

Abstract

Abstract We investigate non-Gaussian noise second-order filtering and fixed-order smoothing problems for non-Gaussian stochastic discrete systems with packet dropouts. We present a novel Kalman-like nonlinear non-Gaussian noise estimation approach based on the packet dropout probability distribution and polynomial filtering technique. By means of properties of Kronecker product we first introduce a second-order polynomial extended system and then analyze the means and variances of the Kronecker powers of the extended system noises. To generate noise estimators in forms of filtering and smoothing, we use the innovation approach. We give an example to illustrate that the presented algorithm has better robustness against packet dropouts than conventional linear minimum variance estimation.

Keywords

• Non-Gaussian stochastic discrete systems
• Packet dropouts
• Polynomial filtering
• Filtering and smoothing
• Innovation approach