Journal of Aeronautical Engineering

Journal of Aeronautical Engineering

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

Document Type : Original Article

Authors
Morteza Moradi
Siroos Talaee
Saeed Nasrollahi
Faculty of Electrical and Computer, Malek-Ashtar University of Technology, Iran.
10.22034/joae.2020.131214
Abstract
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.
 
Keywords
Inertial navigation system
INS/GPS integrated navigation system
Global positioning system
Particle filter
Extended Kalman filter
Hybrid extended Kalman filter
Observability
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Journal of Aeronautical Engineering
Volume 22, Issue 2
December 2020
Pages 41-55

  • Receive Date 30 May 2021