Underwater Navigation System Solution using MEMS-Mobile Sensors during the GPS Outage

Khater, Hatem A.; EL-Sayed, A. T.; El-Shoafy, N.

doi:10.12720/jcm.14.5.375-380

Cited by 2 publications

(1 citation statement)

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“…Therefore, this paper suggests combined filter (CF) data fusion as an integrated navigation solution. Most modern mobile devices include several micro-electromechanical system (MEMS)-INS sensors [ 14 , 15 , 16 ] with accelerometers and gyroscopes that can be used in more effective navigation strategies than INS systems as they minimize cost and scale [ 17 ]. Therefore, inspired by the animal navigation approach, this paper suggests a modern navigation system based on optimized micro-electrical-mechanical system (MEMS)-INS mobile sensors with GPS and a visual navigation brain model (VNBM).…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Combined Visual Navigation Brain Model/GPS/MEMS–INS/ADSFCF Method to Develop Vehicle Independent Guidance Solutions

2021

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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.

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Section: Introductionmentioning

confidence: 99%