Improved LSTM Neural Network-Assisted Combined Vehicle-Mounted GNSS/SINS Navigation and Positioning Algorithm

Abstract

Aiming at the problem of the combined navigation system of on-board GNSS (global navigation satellite system)/SINS (strapdown inertial navigation system), the accuracy of the combined navigation system decreases due to the dispersion of the SINS over time and under the condition of No GNSS signals. An improved LSTM (long short-term memory) neural network in No GNSS signal conditions is proposed to assist the combination of navigation data and the positioning algorithm. When the GNSS signal is normal input, the current on-board combination of the navigation module’s output sensor data information is used for training to improve the LSTM algorithm and to establish the incremental output of the GNSS position of the mapping of the different weights. In No GNSS signal conditions, using the improved LSTM algorithm can improve the combination of navigation and positioning algorithms. Under No GNSS signal conditions, the improved LSTM training model is used to predict the dynamics of SINS information component data. Under No GNSS signal conditions, the combined navigation filtering design is completed, and the error correction of SINS navigation and positioning information is carried out to obtain a more accurate combination of navigation and positioning system accuracy. It can be seen through the actual test experiment using a sports car in the two trajectories under the conditions of No GNSS signals that the proposed algorithm can be compared with the LSTM algorithm. In testing road sections, the proposed algorithm, when compared with the LSTM algorithm to obtain the northward position that the mean square errors were improved by 55.63% and 76.64%, and the eastward position mean square errors were improved by 43.42% and 54.67%. In a straight-line trajectory, improving the effect’s navigation and positioning accuracy and reliability is significant.