By Topic

Kalman Filtering with Innovation Mean Method for INS/GPS Integrated Navigation Systems

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

5 Author(s)
Huaming Qian ; Coll. of Autom., Harbin Eng. Univ., Harbin, China ; Quanxi Xia ; Xuefeng Peng ; Biao Liu
more authors

Being a recursive technique which lends itself to implementation in a microcomputer, the Kalman filter is particularly suitable for on-line estimation. However, when measurement noise covariance R is much larger than process noise Q, the filtering effect will not be satisfied. A new algorithm, which we call innovation mean method, is proposed in this paper. The method makes different periods of measurement update and temporal update. In the measurement update period, innovation and its mean value is calculated. In the temporal update period, prediction update is implemented. Numerical simulation is implemented after the theory is induced. The algorithm is employed in integration navigation system based on global positioning systems (GPS) and inertial navigation system (INS). Simulation results show that the accuracy of innovation mean method is much better than that of basic Kalman filter.

Published in:

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Date of Conference:

19-20 Dec. 2009