Developing a Filtering Algorithm for Doubly Stochastic Images Based on Models with Multiple Roots of Characteristic Equations

Abstract

The properties of doubly stochastic models constructed using a combination of autoregression models with multiple roots of characteristic equations are studied. These models are demonstrated to be adequate to real multidimensional signals; the probabilistic and correlation properties of the simulated signals are studied. Based on the proposed models, a filtering algorithm is developed for doubly stochastic autoregression random fields generated by the models with multiple roots of the characteristic equations. The algorithm is compared to the alternative approaches.