An integration algorithm for SINS/GNSS/Airdata navigation system using adaptive super-twisting method + optimized hybrid GRU-LSTM sequential model

This paper aims to design and implement an adaptive super-twisting observer aided by an optimized hybrid GRU-LSTM sequential model (OHGLSM) for an accurate and reliable integrated navigation system, even in an environment with missing global navigation satellite system (GNSS) signals. The method addresses strap-down inertial navigation system (SINS) perturbations and accounts for unknown inertial measurement errors by integrating extra states into the nominal model. The proposed approach uses the position and velocity components of the GNSS to estimate the states. In this respect, a super-twisting observer is utilized to estimate perturbations along with the state of the system. An adaptive solution is also introduced to adjust the estimator coefficients according to the vehicle's dynamic maneuvers. Innovatively, the OHGLSM, in combination with the air-data sensors, is considered in the observer design to provide reliable vehicle position information during GNSS signal blockages. In this respect, a pitot tube and a barometric pressure sensor are used to define vehicle velocity according to the non-holonomic constraints in addition to altitude data. The proposed method undergoes mathematical analysis and experimental evaluation using real-world vehicle tests in various scenarios to demonstrate its high accuracy and reliability under various conditions. Comparative results with different estimation methods highlight the superior accuracy of the proposed method in constructing a precise navigation system even during GNSS outages.