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

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.

Cubature + Extended Hybrid Kalman Filtering Method and Its Application in PPP/IMU Tightly Coupled Navigation Systems

“…Therefore, the nonlinear filtering methods can be implemented with the PPP/IMU tightly-coupled navigation system to further improve the navigation performance. As shown in [6,7], if the UKF (Unscented Kalman Filter) is implemented with PPP/IMU tightly-coupled navigation systems, the navigation performance is improved during GPS outages compared with the EKF.…”