UKF parameter optimization method using BP neural network for super-mini aerial vehicles

摘要

Owing to the uncertainty in determining the parameters used in unscented transformation which is the main procedure of Unscented Kalman Filter (UKF), we propose a learning method using BP neural network to optimize them adaptively. The experiments were performed on three methods, and the results show that the proposed learning method is better than traditional UKF algorithm, and the precision has an evident increase. The UKF algorithm using BP neural network parameter optimization is effective and feasible, which avoid successfully the lower efficiency and local optimal solution problem in the traditional method.