In a mobile mapping system, the integration of Inertial Navigation System and Global Positioning System is widely applied for determining position and orientation. The Kalman filter or Extended Kalman filter is popularly used for multi-sensor fusion estimation. In those estimation strategies, the linearization and assumption of Gaussian distribution are utilized. However, the fact that the state and measurement models in the integration of Inertial Navigation System and Global Positioning System are originally non-linear and the noise arising during operation may be non-Gaussian distribution. This characteristic may lead to the degraded performance of the system utilizing Kalman filter or Extended Kalman filter in case of non-linear model and non-Gaussian noises. This paper investigates some of non-linear, non-Gaussian estimation strategies in order to improve the performance of an integrated system. Firstly, the fundamental of algorithms will be introduced, those estimation strategies are then applied on simulation scenario to find the optimal method, finally, real measurements collected from field test will be applied to verify the performance of proposed algorithms.
All Science Journal Classification (ASJC) codes
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering