An Intelligent Combined Visual Navigation Brain Model/GPS/MEMS-INS/ADSFCF Method to Develop Vehicle Independent...

This paper presents an integrated navigation system that can function more efficiently than an inertial navigation system (INS), the results of which are not precise enough because of drifts caused by accelerometers. The paper's proposed approach depends primarily on integrating micro-electrical-mechanical system (MEMS)-INS smartphone integrated sensors, the Global Positioning System (GPS), and the visual navigation brain model (VNBM) to enhance navigation in bad weather conditions. The recommended integrated navigation model, using an adaptive DFS combined filter, has been well studied and tested under severe climate conditions on reference trajectories. This integrated technique can easily detect and disable less accurate reference sources (GPS or VNBM) and activate a more accurate one. According to the results, the proposed integrated data fusion algorithm offers a reliable solution for errors in the previous strategies. Furthermore, compared to the pure MEMS-INS method, the proposed system reduces navigational errors by approximately 93.76 percent, whereas the conventional centralized Kalman filter technique reduces such errors by 82.23 percent.