Title :
Sparse-Grid Quadrature $H_infty $ Filter for Discrete-Time Systems with Uncertain Noise Statistics
Author :
Bin Jia ; Ming Xin
Author_Institution :
Dept. of Electr. Eng., Columbia Univ., New York, NY, USA
Abstract :
In This paper H∞ technique is combined with the recently developed sparse-grid quadrature (SGQ) filtering to improve the accuracy and robustness of the state estimation when the noise statistics is not known a priori. The proposed new SGQ H∞ filter (SGQH∞F) is compared with other H∞ filters via two numerical examples. It is shown that the SGQH∞F is more robust and accurate than the extended H∞ filter, unscented H∞ filter, and cubature H∞ filter. In addition it maintains a close performance to the Gauss-Hermite quadrature (GHQ) H∞ filter but is computationally much more efficient since it uses far fewer quadrature points.
Keywords :
H∞ filters; statistical analysis; GHQ H∞ filter; Gauss-Hermite quadrature H∞ filter; SGQH∞F; cubature H∞ filter; discrete-time systems; noise statistics; quadrature points; sparse-grid quadrature H∞ filter; uncertain noise statistics; unscented H∞ filter; Accuracy; Approximation methods; Filtering algorithms; Filtering theory; Noise; Robustness;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
DOI :
10.1109/TAES.2013.6558008