Abstract
Digital mobile mapping, the method that integrates digital imaging with direct geo-referencing, has developed rapidly over the past 15 years. The Kalman filter (KF) is considered an optimal estimation tool for real-time INS/GPS integrated kinematic positioning and orientation determination. However, the accuracy requirements of general mobile mapping applications cannot be easily achieved even when using the KF scheme. Therefore, this study proposes an intelligent scheme combining ANN and RTS backward smoother to overcome the limitations of KF and to enhance the overall accuracy of attitude determination for tactical grade and MEMS INS/GPS integrated systems.
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Abbreviations
- AI:
-
Artificial intelligence
- ANN:
-
Artificial neural networks
- DGPS:
-
Differential global positioning system
- EKF:
-
Extended Kalman filter
- GPS:
-
Global positioning system
- IMU:
-
Inertial measurement unit
- INS:
-
Inertial navigation system
- KF:
-
Kalman filter
- MEMS:
-
Micro-electron mechanical systems
- MFNN:
-
Multi-layer feed-forward neural networks
- PVA:
-
Position, velocity and attitude
- RBF:
-
Radial basis function
- RTS:
-
Rauch–Tung–Striebel
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Acknowledgments
The author would like to acknowledge the financial support by the National Science Council of the Executive Yuan, ROC (Taiwan) (NSC 95-2221-E-006-335-MY2). Dr. Naser El-Sheimy from the MMSS group at the Department of Geomatics Engineering, the University of Calgary, is acknowledged for providing the field test data sets applied in this research. Dr. Eun-Hwan Shin is acknowledged for providing the INS mechanization and INS/GPS extended KF used in this article.
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Chiang, KW., Lin, YC., Huang, YW. et al. An ANN–RTS smoother scheme for accurate INS/GPS integrated attitude determination. GPS Solut 13, 199–208 (2009). https://doi.org/10.1007/s10291-008-0113-0
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DOI: https://doi.org/10.1007/s10291-008-0113-0