Simplified Gauss Hermite Filter Based on Sparse Grid Gauss Hermite Quadrature

Gao Fu Quan, Chen Li Rong, Ding Chuan Hong, Liu Jian Feng

Abstract


In order to improve estimation accuracy of nonliear system with linear measurement model, simplifed gauss hermite filter based on sparse grid gauss hermite quadrature (SGHF) is proposed. Comparing to conventional Gauss-Hermite filter (GHF) based on tensor product gauss quadrature rule, simplified SGHF not only maintains GHF's advantage of precission controllable, high estimation accuracy, but also relieves the curse of dimension problem by reduce the number of gaussian intrgration points to the number of sigma points that scaled unscented transform uses. Theoretical analysis and experimental results show that estimation base on new filter performs significantly beter than extended kalman filter (EKF), and slightly better than unscented kalman filter (UKF) on estimation accuracy and convergence speed, and computational burden is significantly reduced comparing with traditional GHKF.

 

DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3693


Keywords


gasss-hermite quadrature; gauss filter; sparse grid gauss hermite quadrature; bayesian estimation

Full Text:

PDF

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

shopify stats IJEECS visitor statistics