Adaptive extended Kalman filtering applied to low-cost MEMS IMU/GPS integration for UAV

Author

Wang, Xiaogang ; Guo, Jifeng ; Cui, Naigang

Author_Institution

Dept. of Astronaut. Eng., Harbin Inst. of Technol., Harbin, China

fYear

2009

fDate

9-12 Aug. 2009

Firstpage

2214

Lastpage

2218

Abstract

This paper describes the adaptive extended Kalman filtering which is applied to low-cost MEMS IMU/GPS integration. Unmanned Aerial Vehicles are versatile flying machine capable of handling both military and civilian missions. The availability of low-cost MEMS IMU has made it possible to construct inexpensive, integrated systems for usage in UAV applications. The adaptive extended Kalman filtering is applied to fuse the information from low-cost MEMS IMU and a Global Positioning System receiver. The maximum likelihood estimator of Myers and Tapley which could be used to online estimate the process noise is presented. Finally, the simulation result shows the effectiveness of adaptive extended Kalman filtering.

Keywords

Global Positioning System; adaptive Kalman filters; inertial navigation; maximum likelihood estimation; nonlinear filters; remotely operated vehicles; space vehicles; Global Positioning System receiver; UAV application; adaptive extended Kalman filtering; low-cost MEMS IMU/GPS integration; maximum likelihood estimator; process noise; unmanned aerial vehicle; versatile flying machine; Adaptive filters; Equations; Filtering; Global Positioning System; Kalman filters; Magnetic sensors; Micromechanical devices; Noise level; Noise measurement; Unmanned aerial vehicles; Adaptive extended Kalman filteing; Low-cost MEMS IMU; UAV;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronics and Automation, 2009. ICMA 2009. International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4244-2692-8

Electronic_ISBN

978-1-4244-2693-5

Type

conf

DOI

10.1109/ICMA.2009.5246654

Filename

5246654