Aitken based modified Kalman filtering stochastic gradient algorithm for dual-rate nonlinear models

摘要

This paper develops an Aitken based modified Kalman filtering stochastic gradient algorithm for dual-rate nonlinear models. The Aitken based method can increase the convergence rate and the modified Kalman filter can improve the estimation accuracy. Thus compared to the traditional auxiliary model based stochastic gradient algorithm, the proposed algorithm in this paper is more effective, and this is proved by the convergence analysis. Furthermore, two simulated examples are given to illustrate the effectiveness of the proposed algorithm. (C) 2019 Published by Elsevier Ltd on behalf of The Franklin Institute.