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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40756

Title: Neural network aided adaptive Kalman filtering for GPS applications
Authors: Dah-Jing Jwo;Chi-Shui Chang;Chia-Hsin Lin
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Date: 2004
Issue Date: 2017-02-06T07:38:12Z
Publisher: Systems, Man and Cybernetics, 2004 IEEE International Conference on
Abstract: Abstract: The Kalman filtering theory plays an important role in the fields of navigation system and receiver tracking loop designs. For obtaining optimal (in the viewpoint of minimum mean square error) estimate of the system state vector, the designers are required to have exact knowledge on both dynamic process and measurement models, in addition to the assumption that both the process and measurement are corrupted by zero mean Gaussian white noises. The neural network can be incorporated into the filtering mechanism as a dynamic model corrector for identifying the real-time nonlinear dynamics modeling error when the modeling uncertainty is considered. The partially unknown part of the dynamics is identified by the neural network and the modeling error is compensated. Applications of the neural network aided adaptive Kalman filter is introduced to the GPS navigation and receiver tracking loop design.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/40756
Appears in Collections:[通訊與導航工程學系] 演講及研討會

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