Comparison of Extended Kalman Filter with Extended Particle Filter in Loosely Coupled INS/GPS Integration

Inertial Navigation System (INS) estimates states which are position, velocity and attitude using accelerometers, gyroscopes data and initial conditions. These sensors have errorswhich INS accumulates them. Therefor INS output error increase rapidly with time. For reducing these errors, INS should be integrated with otheraided sensors such as GPS. INS/GPS Integration increases states accuracy and classified to three types such as looselycoupled, tightly coupled and ultra-tight integration. The cheapest and common method is loosely coupled integration. Combining these two sensors in loosely coupled integrationneeds a filter or an estimation method. Common methods for this purpose are Kalman filters and Particle filters.Kalman filters linearize states and they model noises with the Gaussian distribution. Particle filter estimates states based onMonte Carlo principle which states that every noise or data distribution could be described by many samples called particles. In this paper, two estimation algorithm, extended kalman filter(EKF) from KFs family and extended particle filter (EPF) from PFs family are implemented for loosely coupled INS/GPSintegration. Loosely coupled INS/GPS in a 111.42 meter trajectory for 185 seconds is implemented with EKF and EPF. At last, the position errors and results are compared.

کلیدواژه ها:

Inertial navigations system (INS) ، loosely coupled INS ، GPS integration ، Extended Kalman filter (EKF) ، Extended Particle filter (EPF)