Comparison of Particle Filter Pperformance with Extended and Hybrid Extended Kalman Filter in INS/GPS Data Fusion

The error of the inertial navigation system (INS) increases with time and leads the navigation system to instability. Hence, this paper investigates INS/GPS integration. Kalman filter is the most common way for integrating these two systems, but due to the nonlinear behavior of the INS/GPS integrated navigation system; nonlinear filters are used for data integration. Furthermore, given that GPS is capable of measuring the velocity and position of the object, these measurements are used to estimate system states (position, velocity, and orientation). In the following, we have investigated the observability of the system’s state space. Using practical data from a UAV, simulation results shows that the performance of the particle filter is better than that of the other two estimators for complex nonlinear systems with non-Gaussian noise.