Novel Hybrid Algorithm of Improved CKF and GRU for GPS/INS
Dengao Li, Yuqi Wu, Jumin Zhao
Abstract
In order to ensure that Inertial Navigation System/Global Positioning System integrated navigation system (INS/GPS) can still provide high precision positioning results when GPS outages, a novel hybrid algorithm based on Gated Recurrent Unit (GRU) and interacting multiple model adaptive robust cubature Kalman filter (IMM-ARCKF) is proposed. Firstly, the IMM-ARCKF algorithm is proposed to solve the uncertainty of system model and measurement noise statics in the application of INS/GPS on the road. Then, GRU neural network is introduced into INS/GPS system which includes two modes of training and prediction. When GPS signal can be received, the GRU neural network works in the training mode. When GPS outages, the GRU neural network predicts the GPS position increment. Finally, the effectiveness of the algorithm is evaluated by the experiment and analysis. From the data of the experiment, the proposed algorithm can improve the positioning accuracy during GPS outages.