Neural networks for pattern recognition
Neural networks for pattern recognition
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Towards the automation of intelligent data analysis
Applied Soft Computing
New fuzzy wavelet neural networks for system identification and control
Applied Soft Computing
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Digital mobile mapping, which integrates digital imaging with direct geo-referencing, has developed rapidly over the past fifteen years. The direct geo-referencing is the determination of time variable position and orientation parameters for a mobile digital imager. The most common technologies used for this purpose today are satellite positioning by GPS and inertial navigation using an IMU. They are usually integrated in such a way that the GPS receiver is the main position sensor, while the IMU is the main orientation sensor. KF is considered the optimal estimation tool for teal-time INS/GPS integrated kinematic positioning and orientation determination. In post-mission processing, on the other hand, data from the whole mission can be used to estimate the trajectory. When filtering is used in the first step, an optimal smoothing algorithm can be applied to achieve higher accuracy for mobile mapping applications. An intelligent and hybrid scheme consisting of an ANN and KF is proposed to overcome the limitations of KF and to improve the performance of an INS/GPS integrated system from a previous study. However, the accuracy requirements of general mobile mapping applications cannot be achieved easily even by using an ANN-KF scheme. Therefore, this study proposes an ANN embedded RTS backward smoother to enhance the overall accuracy of POS parameters for a tactical grade INS/GPS integrated system in a post-mission mode. Combing the tactical grade INS/GPS integrated system and intelligent POS scheme proposed in this study, a cheap but reasonably accurate POS can be anticipated.