Volume XL-2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2, 127-132, 2014
https://doi.org/10.5194/isprsarchives-XL-2-127-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2, 127-132, 2014
https://doi.org/10.5194/isprsarchives-XL-2-127-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  11 Nov 2014

11 Nov 2014

Non-Linear Filtering for Precise Point Positioning GPS/INS integration

M. Abd Rabbou and A. El-Rabbany M. Abd Rabbou and A. El-Rabbany
  • Department of Civil Engineering, Ryerson University, 350 Victoria Street, Toronto, Ontario, M5B 2K3, Canada

Keywords: GPS, PPP, INS, EKF, UKF, PF and tightly coupled

Abstract. This research investigates the performance of non-linear estimation filtering for GPS-PPP/MEMS-based inertial system. Although integrated GPS/INS system involves nonlinear motion state and measurement models, the most common estimation filter employed is extended Kalman filter. In this paper, both unscented Kalman filter and particle filter are developed and compared with extended Kalman filter. Tightly coupled mechanization is adopted, which is developed in the raw measurements domain. Un-differenced ionosphere-free linear combination of pseudorange and carrier-phase measurements is employed. The performance of the proposed non-linear filters is analyzed using real test scenario. The test results indicate that comparable accuracy-level are obtained from the proposed filters compared with extended Kalman filter in positioning, velocity and attitude when the measurement updates from GPS measurements are available.