Dual optimization vehicle positioning algorithm based on neural network prediction

To address the performance degradation of global positioning system (GPS)/ inertial navigation system (INS) integrated navigation system caused by GPS signal outages, a dual optimization vehicle positioning algorithm based on neural network prediction is proposed. Firstly, a convolutional neural network-gated recurrent unit (CNN-GRU) prediction model is constructed, the INS data features are extracted by CNN, and the pseudo-signals during GPS outages are predicted by GRU. Secondly, to enhance the performance of neural network, the inertial measurement unit data is preprocessed by empirical mode decomposition threshold filtering, and the hyperparameters of the network are optimized by the white shark optimizer (WSO). Then, based on the relationship between the internal parameters of the extended Kalman filter (EKF) and the optimal estimation error of GPS/INS, error compensation model is established by GRU to further optimize the positioning accuracy during GPS outages. The experimental results show that compared with CNN-GRU+EKF, CNN-LSTM+KF and GRU+AKF, the proposed algorithm reduces the average root mean square error of positioning distance by 62.00%, 68.31%, and 74.80% under 30 s and 150 s GPS outages, respectively, validating its effectiveness in scenarios with GPS signal denial. © 2025 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.