Intelligent MEMS INS/GPS integration for land vehicle navigation
Date
2006
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Abstract
Although Global Positioning System (GPS) has been widely used to land vehicle navigation systems, GPS is unable to provide continuous and reliable navigation solutions in the presence of signal fading and/or blockage such as in urban areas. With the advent of the Micro-Electro-Mechanical System (MEMS) Inertial Navigation System (INS), a low-cost MEMS INS/GPS integration system becomes available to provide improved navigation performance by integrating the long-term GPS accuracy with the short-term INS accuracy. The challenges to low-cost MEMS INS/GPS integration arise from dealing with the corrupted GPS data in signal-degraded environments, the large instrument errors experienced with low-grade MEMS sensors and the distorted magnetic measurements from an embedded electronic compass. This dissertation develops intelligent data fusion and processing techniques for such a low-cost integration system by incorporating the Artificial Intelligence (AI) with the Kalman filtering. Two cascaded Kalman filters implemented upon a loosely coupled integration scheme are applied to perform data fusion in the velocity/attitude and position domain, respectively. Three AI-based methods are developed for GPS data assessment, INS error control and compass error modelling to enhance the Kalman-filter-based integration. Specifically, a fuzzy GPS data classification system is developed to optimize INS/GPS data fusion through adjusting the measurement covariances of the Kalman filters according to GPS signal degradation conditions. A dynamics knowledge aided inertial navigation algorithm along with a fuzzy expert vehicle dynamics identification system is created to reduce and control INS error drift throu gh simplifying system models and extending measurement update schemes of the Kalman filters. A neural-networks-based compass calibration algorithm is developed to provide the correct compass heading updates to the Kalman filters in the presence of disturbance. The developed algorithms have been tested and evaluated in various GPS conditions, which include open areas, complete GPS outages and urban areas, using a low-cost Xsens MT9 MEMS IMU and SiRF Star II conventional/high sensitivity GPS receivers. The obtained results have confirmed the effectiveness of the AI-based methods and the significant performance improvement by the intelligent integration algorithm. For GPS outages around 3 minutes, the intelligent integration system is able to maintain satisfactory position accuracy with the maximum error less than 30 m. In the typical North American urban canyons, the intelligent integration system can provide continuous and reliable navigation solutions with the horizontal position accuracy of around 15 m. Overall results confirm the benefits and advantages of applying the developed AI methods to assist the low-cost MEMS INS/GPS integration for land vehicle navigation.
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Bibliography: p. 204-214
Some pages are in colour.
Some pages are in colour.
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Citation
Wang, J. (2006). Intelligent MEMS INS/GPS integration for land vehicle navigation (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/739