In this paper, the multi-sensor data fusion technique based on fuzzy clustering is used to fuse the data from low cost MEMS IMUs to build an INS model. Using this model, the inertial navigation data PVA is extracted. The navigation data PVA is integrated with the GPS data using Kalman filters to build an accurate navigation system of an UAV. Simulation results show that the method can achieve higher accuracy solutions with low cost IMU sensors and improve the performance of integrated navigation system.
Abosekeen, A., & Abdalla, A. (2012). Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System. The International Conference on Electrical Engineering, 8(8th International Conference on Electrical Engineering ICEENG 2012), 1-23. doi: 10.21608/iceeng.2012.30810
MLA
A. D. Abosekeen; A. E. Abdalla. "Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System". The International Conference on Electrical Engineering, 8, 8th International Conference on Electrical Engineering ICEENG 2012, 2012, 1-23. doi: 10.21608/iceeng.2012.30810
HARVARD
Abosekeen, A., Abdalla, A. (2012). 'Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System', The International Conference on Electrical Engineering, 8(8th International Conference on Electrical Engineering ICEENG 2012), pp. 1-23. doi: 10.21608/iceeng.2012.30810
VANCOUVER
Abosekeen, A., Abdalla, A. Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System. The International Conference on Electrical Engineering, 2012; 8(8th International Conference on Electrical Engineering ICEENG 2012): 1-23. doi: 10.21608/iceeng.2012.30810
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