By Topic

Quantitative comparison between Kalman filter and Particle filter for low cost INS/GPS integration

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
$33 $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)
Jacques Georgy ; NavINST - Navigation and Instrumentation Research Group, Electrical and Computer Engineering Department, Queen's University, Canada ; Umar Iqbal ; Aboelmagd Noureldin

Technological advances in both GPS and low cost micro-electro mechanical system (MEMS)-based inertial sensors enabled monitoring the location of moving platforms for numerous positioning and navigation (POS/NAV) applications. When miniaturized inside any moving platforms, MEMS-based inertial navigation system (INS) can be integrated with GPS and enhance the performance in denied GPS environments (like in urban canyons). The combination of the two systems, traditionally performed by Kalman filtering (KF), exploits their complementary characteristics. Due to the inherent errors of MEMS inertial sensors and the relatively high noise levels associated with their measurements, KF has limited capabilities in providing accurate positioning. Particle filtering (PF) was suggested to accommodate for arbitrary inertial sensor characteristics, motion dynamics and noise distributions. This article gives detailed comparison between KF and PF as applied to MEMS-based INS/GPS integration and examines the performance of both methods during a road test experiment.

Published in:

Mechatronics and its Applications, 2009. ISMA '09. 6th International Symposium on

Date of Conference:

23-26 March 2009