A BiLSTM-Based Multiscale Convolutional Attention Method for Pseudorange Compensation in GNSS/INS Tightly Coupled Integration

To address the decline in positioning accuracy caused by long-term global navigation satellite system (GNSS) observation outages under tightly coupling (TC) conditions in urban canyons, this article proposes a pseudorange compensation mechanism based on a bidirectional long short-term memory network with multiscale convolutional attention (BiLSTM-MSCA). Under frequent occlusion of satellite signals, the proposed network is used to learn the pseudorange incremental relationship between inertial navigation system (INS) information and GNSS signals, and then compensate for the GNSS pseudorange observations. The proposed BiLSTM-MSCA utilizes the bidirectional information of input INS and GNSS signals in the time domain and enhances the extraction of key information to improve the prediction accuracy of the network. Experiments based on the measured data of urban canyons show that the horizontal positioning accuracy of the proposed method is improved by 20% compared with the existing neural network-assisted method under the condition of 100-s GNSS observation loss.