Development of an algorithm for tuning a suboptimal scaling factor in the SINS filtering problem

Abstract

A modified strong tracking unscented Kalman filter for nonlinear dynamical systems is proposed. A matrix of the suboptimal scaling factor is introduced into the prediction covariance to ensure evaluation stability and smoothness at appearance of the process model uncertainty. It is shown that the use of a fuzzy algorithm to adjust the softening coefficient in real time allows to avoid the loss of accuracy in the segments in which the process model is defined. As a result of modeling the SINS correction task, it was found that the proposed fuzzy filter has good evaluation smoothness and high accuracy. Keywords SINS; suboptimal scaling factor; softening coefficient; fuzzy Takagi — Sugeno model