Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter
AbstractIntegration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Rabbou, M.A.; El-Rabbany, A. Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter. Sensors 2015, 15, 7228-7245.
Rabbou MA, El-Rabbany A. Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter. Sensors. 2015; 15(4):7228-7245.Chicago/Turabian Style
Rabbou, Mahmoud A.; El-Rabbany, Ahmed. 2015. "Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter." Sensors 15, no. 4: 7228-7245.