Mohamed Heba G, Khater Hatem A, Moussa Karim H
Electrical Department, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Electrical Department, College of Engineering, Alexandria Higher Institute of Engineering and Technology, Alexandria 21421, Egypt.
Micromachines (Basel). 2021 Jun 18;12(6):718. doi: 10.3390/mi12060718.
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.
本文提出了一种集成导航系统，其运行效率高于惯性导航系统（INS），因为加速度计引起的漂移，惯性导航系统的结果不够精确。本文提出的方法主要依赖于集成微机电系统（MEMS）-惯性导航系统智能手机集成传感器、全球定位系统（GPS）和视觉导航脑模型（VNBM），以增强恶劣天气条件下的导航能力。推荐的集成导航模型采用自适应深度优先搜索（DFS）组合滤波器，已在恶劣气候条件下对参考轨迹进行了充分研究和测试。这种集成技术可以轻松检测并禁用不太准确的参考源（GPS或VNBM），并激活更准确的参考源。根据结果，所提出的集成数据融合算法为先前策略中的误差提供了可靠的解决方案。此外，与纯MEMS-惯性导航系统方法相比，所提出的系统将导航误差降低了约93.76%，而传统的集中卡尔曼滤波技术将此类误差降低了82.23%。
