An Integrated INS/GNSS System With an Attention-Based Deep Network for Drones in GNSS Denied Environments

We propose a neural network-based approach to assist positioning in an integrated inertial navigation system/global navigation satellite system (INS/GNSS) during GNSS interruptions. One can aid a navigation system during GNSS outages by substituting the GNSS measurements with a well-trained neural network (NN). Outputs of this NN can then be used in a Kalman filtering scheme to acquire the best consistent and concise estimates according to the INS measurements. Since this problem has inherent spatial and temporal aspects, the proposed NN should account for both aspects simultaneously. Convolutional long short-term memory (CLSTM) structure is an excellent candidate to satisfy the aforementioned requirement. The designed CLSTM architecture uses the angular rates and specific forces measured by INS to output the pseudo GNSS position increment covering for the lost GNSS signal. An attention mechanism is added to the final layer of the CLSTM to counter the gradient vanishing problem in long time-series prediction. A field test utilizing a fixed-wing unmanned aerial vehicle is arranged to evaluate the proposed architecture's performance. The field test result shows a significant 3D positioning improvement during GNSS outages.