Research on the Unscented Kalman Filter for GPS /INS

摘要

We extend the use of the UKF to a broader class of nonlinear estimation problems, including nonlinear system identification, training of neural networks, and dual estimation problems. Our preliminary results were presented in this paper. The algorithms are further developed and illustrated with a GPS/INS dynamic system. The UKF algorithm is unbiased and minimum variance, which is better than the EKF method in GPS/INS dynamic system. The UKF is suggested for debasing linearization bias for the nonlinear measurement equations. Physical systems are often subjected to unexpected deviations or failures. As a result, it is important to have a method in order to maintain an accurate and reliable solution. The UKF control approach is applied to detect the blunders in GPS/INS navigation and kinematic positioning.