COMPARISON OF GPS SMOOTHENING METHODS BETWEEN EXTENDED KALMAN FILTER AND PARTICLE FILTER FOR UAV

This paper presents a method for smoothing GPS data from a UAV using Extended Kalman filtering and particle filtering for navigation or position control. A key requirement for navigation and control of any autonomous flying or moving robot is availability of a robust attitude estimate. Consider a dynamic system such as a moving robot. The unknown parameters, e.g., the coordinates and the velocity, form the state vector. This time dependent vector may be predicted for any instant time by means of system equations. The predicted values can be improved or updated by observations containing information on some components of the state vector. The whole procedure is known as Kalman filtering. On the other hand, the particle filtering algorithm is to perform a recursive Bayesian filter by Monte Carlo simulations. The key is to represent the required posterior density function by a set of random samples, which is called particles with associated weights, and to compute estimates based on these samples as well as weights. We compare the two GPS smoothening methods: Extended Kalman Filter and Particle Filter for mobile robots applications. Validity of the smoothing methods is verified from the numerical simulation and the experiments. The numerical simulation and experimental results show the good GPS data smoothing performance using Extended Kalman filtering and particle filtering.