By Topic

MEMS IMU and two-antenna GPS integration navigation system using interval adaptive Kalman filter

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xiufeng He ; Civil Eng., Hohai Univ., Nanjing, China ; Yang Le ; Wendong Xiao

For a nonlinear integrated GPS/IMU system with an uncertain dynamic model, the standard extended Kalman filtering algorithm is no longer applicable. In this research, an interval filtering algorithm is applied to the uncertain integrated system. The system parameters uncertainties are described by intervals. The IAKF algorithm is established for the uncertain integrated system. The IAKF algorithm has the same structure as the standard extended Kalman filtering algorithm. The testing results indicate that the IAKF algorithm is effective for the uncertain nonlinear integrated system, and it can be used to test the chosen parameters of an integrated GPS/IMU system. Thus, the IAKF algorithm has good potential in real-time applications for nonlinear integrated systems with parameter and noise uncertainties.

Published in:

Aerospace and Electronic Systems Magazine, IEEE  (Volume:28 ,  Issue: 10 )