Title :
Adaptive H2/H∞ filter for integrated navigation system
Author :
Xiao-guang Liu ; Jing-tao Hu ; Tao-chang Li ; Xiao-Ping Bai ; Lei Gao
Author_Institution :
Shenyang Inst. of Autom., Shenyang, China
Abstract :
Conventional H∞filter is much more conservative, as the filter parameters are set in the initial while Kalman filter requires the statistical properties of noise accurately. For the above limitations, this paper proposed an adaptive H2/H∞ filter for multi-sensor integrated navigation systems. The method derives the gain weight coefficient using the theory of matrix inequalities and estimation variance matrix based on the least trace criterion. The method can improve the accuracy and robustness of integrated navigation systems by adjusting gain weight coefficient automatically. Finally, the adaptive H2/H∞ filter method is compared with Kalman filter and H∞ filter on the test platform, Results show that the adaptive H2/H∞ filter can provide better performance of integrated navigation systems by combining the advantages of the Kalman filter and H∞ filter.
Keywords :
H∞ filters; H2 filters; Kalman filters; adaptive filters; agricultural machinery; estimation theory; linear matrix inequalities; navigation; sensor fusion; Kalman filter; adaptive H∞ filter; adaptive H2 filter; estimation variance matrix; gain weight coefficient; least trace criterion; matrix inequalities; multisensor integrated navigation systems; noise statistical properties; Filtering algorithms; Filtering theory; Global Positioning System; Kalman filters; Robustness; Adaptive; Data fusion; H2/H∞filter; Integrated navigation;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7053055