comparison of nonlinear filtering techniques for inertial sensors error identification in ins/gps integration

Nonlinear filtering techniques are used to fuse the global positioning system (gps) and the inertial navigation system (ins) together to provide a robust and reliable navigation system with a performance superior to that of either ins and gps alone. prominent nonlinear estimators in this field are the kalman filters (kf) and particle filters (pf). the main objective of this research is the comparative study of the wellestablished filtering methods of ekf, ukf and the pf based on ekf and ukf in insgps integrated navigation system. different features of insgps integrated navigation methods in the state estimation, bias estimation and bias/scale factor estimation are investigated by using these four filtering algorithms. both groundvehicle experimental test and flight simulation test have been utilized to evaluate the filters performance