Altitude Prediction Method Using CNN-LSTM for GNSS/INS/Barometer Integrated Navigation During GNSS Outages

The global navigation satellite system (GNSS), inertial navigation system (INS), and barometer integrated navigation system is widely used in the unmanned aerial vehicles (UAVs). As UAV applications extend into complex low-altitude areas, GNSS signals may be obstructed by terrain, leading to GNSS outages. These outages are characterized by a loss of GNSS signal availability, causing a degradation of positioning accuracy under the INS/Barometer system. Therefore, GNSS measurement prediction during GNSS outages is essential. Since both the INS and barometer have limited altitude accuracy, altitude prediction becomes particularly critical. This paper establishes a GNSS measurement prediction network with separate horizontal and altitude channels. The altitude prediction channel is trained using GNSS and barometer data to improve accuracy. The network combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to enhance the extraction of features from different sensors. Simulation results demonstrate that, during GNSS outages, positioning based on the predicted GNSS altitude using the proposed method improves altitude accuracy by 76.06% (RMSE) compared to traditional INS/Barometer-based methods.