Enhancing Integrated Navigation with a Self-Attention LSTM Hybrid Network for UAVs in GNSS-Denied Environments

Performing long-duration navigation without the global navigation satellite system (GNSS) network is a challenging task, particularly for small unmanned aerial vehicles (UAVs) equipped with low-cost micro-electro-mechanical sensors. This study proposes a hybrid neural network that integrates self-attention mechanisms with long short-term memory (SALSTM) to enhance GNSS-denied navigation performance. The estimation task of GNSS-denied navigation is first modeled based on UAV aerodynamics and kinematics, enabling a precise definition of the inputs and outputs that SALSTM needs to map. A self-attention layer is inserted in multiple LSTM layers to capture long-range dependencies in subtle dynamic changes. The output layer is designed to generate state sequences, leveraging the recursive nature of LSTM to enforce state continuity constraints. The outputs of SALSTM are fused to enhance integrated navigation within an extended Kalman filter framework. The performance of the proposed method is evaluated using flight data obtained from field tests. The results demonstrate that SALSTM-enhanced integrated navigation achieves superior long-term stability and improves velocity and position estimation accuracy by more than 50% compared to the best existing methods.