Research on dynamic correlation localization algorithm based on UKF improvement

An improved dynamic localisation algorithm with UKF is proposed to address the problem of noise effects in dynamic environments leading to abnormalities in the solution of the localisation algorithm. The algorithm replaces the traditional EKF algorithm with the UKF algorithm, which can be applied to non-linear systems in non-ideal situations, and the UKF does not require the cumbersome Jacobi matrix in the EKF process, while the noise adaptive factor is added to the UKF to improve the noise reduction capability of the algorithm, and the adaptive capability of the algorithm is improved in the time update and measurement update phases, which is more accurate in the actual test to estimate the The system state is more accurately estimated in practical tests. The simulation results show that the algorithm can estimate the system state more accurately than the traditional extended Kalman filter algorithm, and can be applied to the relevant positioning algorithm to improve the positioning accuracy and increase the trajectory smoothness.