By Topic

The MEMS IMU Error Modeling Analysis Using Support Vector Machines

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

2 Author(s)
Guoqiang Xu ; Beijing Inst. of Technol., Aerosp. Acad., Beijing, China ; Xiuyun Meng

It's well known that the accuracy of the inertial navigation systems will rapidly degrades with time because of the measure sensor's error. Several variance techniques have been devised for the error modelling of this error by way of weighting functions, PSD, ARMA and NNs, etc. In this paper, we use the SVM(support vector machine) technique to predict the future noise coming from the measure sensors especially the gyro. Then we compare the resulting noise data with the one coming from the ARMA model and NNs model. Finally the three models are compensated to the output data from the IMU to compute the position errors and attitude angle errors. The results indicate that the SVR model (support vector regression) shows more stable feature and is more adequate for long time navigation than the AR model and NNs model.

Published in:

Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on  (Volume:1 )

Date of Conference:

Nov. 30 2009-Dec. 1 2009