Abstract
When natural disasters such as earthquakes occur, accurate navigation and positioning information may not be available, making a purely inertial pedestrian navigation system particularly important for rescuers. In this paper, researchers propose a zero-velocity update architecture for pedestrian navigation based on the Informer model, which is integrated into wearable devices. This architecture modifies the fully connected layer of the Informer model to be used for the binary classification task of the zero-velocity update method (ZUPT), allowing for accurate identification of gait information at each moment using only inertial measurement data. By wearing the device on the foot during natural disasters like earthquakes, the location of the pedestrian can be more accurately determined, facilitating rescue efforts. During the experimental process, a Kalman filter model was constructed to achieve zero-velocity updating of the pedestrian's motion trajectory. A 2000 m walking experiment and a 210 m mixed-gait experiment were conducted to accurately identify gait information at each moment, thereby reducing the cumulative error of the inertial system. Subsequently, a convolutional neural network (CNN) model and a model combining CNN with a long short-term memory network (CNN + LSTM) were introduced as comparative experiments to verify the performance of the proposed architecture. The experimental results demonstrate that the proposed architecture enhances the adaptability of the zero-velocity update algorithm in underground or sheltered spaces, with all results outperforming the other two models.