Vehicle navigator using a mixture particle filter for inertial sensors/odometer/map data/GPS integration

• Published in: IEEE transactions on consumer electronics Vol. 58; no. 2; pp. 544 - 552
• Main Authors: Georgy, J., Noureldin, A., Goodall, C.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometers; Degradation; Error detection; Geographic information systems; Global Positioning System; GPS; Inertial; Inertial Sensors; Land Vehicle Navigation; Map Matching; Mathematical model; Mathematical models; Particle Filter; Product design; Satellite navigation systems; Sensors; Stochastic processes; Vehicles
• ISSN: 0098-3063; 1558-4127
• DOI: 10.1109/TCE.2012.6227459

The market for vehicular navigators boomed over the last few years. These navigators rely mainly on satellite based navigation systems such as the Global Positioning System (GPS) to assist drivers. Due to interruption or degradation in such systems in dense urban scenarios, they have to be augmented with other systems to achieve continuous and accurate vehicular navigation. GPS is integrated with low-cost micro-electro mechanical system (MEMS)-based inertial sensors. However, these sensors provide inadequate performance in degraded GPS environments because of their complex error characteristics that often lead to large position drift errors. This paper proposes a continuous and accurate solution integrating low-cost MEMS-based inertial sensors, the vehicle odometer, GPS, and map data from road networks. Despite the traditional inadequate performance of MEMS-based sensors in this problem, the performance is enhanced through: (i) a special combination of inertial sensors and odometer that has better performance for land vehicles than traditional solutions; (ii) The use of map information from road networks to constrain the positioning solution; (iii) The use of an advanced particle filtering (PF) technique to perform the integration, which work with nonlinear models and better modeling of inertial sensor errors, in addition to better integration with the map data. The performance of the proposed positioning system has been verified extensively on real road tests in downtown trajectories with degraded or totally denied GPS for long durations.