Abosekeen, A., Abdalla, A. (2012). Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System. test, 8(8th International Conference on Electrical Engineering ICEENG 2012), 1-23. doi: 10.21608/iceeng.2012.30810
A. D. Abosekeen; A. E. Abdalla. "Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System". test, 8, 8th International Conference on Electrical Engineering ICEENG 2012, 2012, 1-23. doi: 10.21608/iceeng.2012.30810
Abosekeen, A., Abdalla, A. (2012). 'Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System', test, 8(8th International Conference on Electrical Engineering ICEENG 2012), pp. 1-23. doi: 10.21608/iceeng.2012.30810
Abosekeen, A., Abdalla, A. Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System. test, 2012; 8(8th International Conference on Electrical Engineering ICEENG 2012): 1-23. doi: 10.21608/iceeng.2012.30810

Fusion of Low-Cost MEMS IMU/GPS Integrated Navigation System

The International Conference on Electrical Engineering
Article 64, Volume 8, 8th International Conference on Electrical Engineering ICEENG 2012, May 2012, Pages 1-23    PDF (814.66 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2012.30810
Authors
A. D. Abosekeen1; A. E. Abdalla2
1Master Student,M.T.C, Egyptian Air Forces.
2Assoc. Prof, Dean of M.T.C,M.T.C, Egyptian Air Forces.
Abstract
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.
Keywords
Data fusion; fuzzy; data clustering; Inertial Navigation system (INS); Global Navigation system(GPS); Kalman Filter(KF); andINS/GPS Integration
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