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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50057

Title: Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning
Authors: Dah-Jing Jwo
Hung-Chih Huang
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: GPS
Extended Kalman filter
Neural network
Date: 2004-09
Issue Date: 2018-09-14T01:23:57Z
Publisher: The Journal of Navigation
Abstract: Abstract: The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.
Relation: 57(3) pp.449-463
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50057
Appears in Collections:[通訊與導航工程學系] 期刊論文

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