Particle filter based integrated navigation for unmanned aerial vehicles

In this work, a particle filter based integrated navigation system is designed. Kalman Filter has significant assumptions such as linearity of system and measurement models and Gaussian distributions. Conventionally, Kalman Filter based algorithms is used for navigation systems which is highly non-linear. This situation causes that Kalman Filter does not work effective. Nowadays, particle filter based algorithms take over with respect to other estimation algorithms because of progression of computer's processors capability. Within the scope of this thesis, a particle filter based integrated navigation system is designed. This designed navigation system is tested with the test senarios which is include both simulations tests and real system tests. Within this context, the error margin is reduced a quarter according to the GPS-based systems.