Sequential Monte Carlo methods for navigation systems

The paper deals with new approach to navigation information processing using Sequential Monte Carlo Methods known as particle filtering. Although, the Sequential Monte Carlo Methods require huge amount of computing, these methods are more efficient than Kalman filters especially when the system is nonlinear or if probability density function of the errors is non-Gaussian. The paper presents integration of Inertial Navigation System (INS) and Global Positioning System (GPS) using Sequential Monte Carlo Methods for navigation information processing. Navigation systems were created in simulation environment. An original asset of the work consists in creation of models in the simulation environment to confirm the algorithms.