By Topic

Performance Enhancement of MEMS-Based INS/GPS Integration for Low-Cost Navigation Applications

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

4 Author(s)
Aboelmagd Noureldin ; Dept. of Electr. & Comput. Eng., Queen's Univ., Kingston, ON ; Tashfeen B. Karamat ; Mark D. Eberts ; Ahmed El-Shafie

The relatively high cost of inertial navigation systems (INSs) has been preventing their integration with global positioning systems (GPSs) for land-vehicle applications. Inertial sensors based on microelectromechanical system (MEMS) technology have recently become commercially available at lower costs. These relatively lower cost inertial sensors have the potential to allow the development of an affordable GPS-aided INS (INS/GPS) vehicular navigation system. While MEMS-based INS is inherently immune to signal jamming, spoofing, and blockage vulnerabilities (as opposed to GPS), the performance of MEMS-based gyroscopes and accelerometers is significantly affected by complex error characteristics that are stochastic in nature. To improve the overall performance of MEMS-based INS/GPS, this paper proposes the following two-tier approach at different levels: (1) improving the stochastic modeling of MEMS-based inertial sensor errors using autoregressive processes at the raw measurement level and (2) enhancing the positioning accuracy during GPS outages by nonlinear modeling of INS position errors at the information fusion level using neuro-fuzzy (NF) modules, which are augmented in the Kalman filtering INS/GPS integration. Experimental road tests involving a MEMS-based INS were performed, which validated the efficacy of the proposed methods on several trajectories.

Published in:

IEEE Transactions on Vehicular Technology  (Volume:58 ,  Issue: 3 )