Particle Filtering: A Priori Estimation of Observational Errors of a State-Space Model with Linear Observation Equation

Observational errors of Particle Filtering are studied over the case a state-space model with linear observation equation. In this study, observational estimated prior to upcoming observations. This action is added basic algorithm filter as new step for acquisition state estimations. intervention useful in presence missing data problems mainly, well sample tracking impoverishment issues. It applies theory Homogeneous and Non-Homogeneous closed Markov Systems study particle distribution domain and, thus, lays foundations employment stochastic control against impoverishment. A simulating example quoted demonstrate effectiveness proposed method comparison existing ones, showing that able combine satisfactory precision results low computational cost provide an achieve prediction tracking.