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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50060

Title: Applying Back-propagation Neural Networks to GDOP Approximatio
Authors: Dah-Jing Jwo
Kuo-Pin Chin
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: GPS
Date: 2002-01
Issue Date: 2018-09-14T01:43:23Z
Publisher: The Journal of Navigation
Abstract: Abstract: In this paper, back-propagation (BP) neural networks (NN) are applied to the GPS satellite Geometric Dilution of Precision (GDOP) approximation. The methods using BPNN are general enough to be applicable regardless of the number of satellite signals being processed by the receiver. BPNN is employed to learn the functional relationships firstly, between the entries of a measurement matrix and the eigenvalues and thus generate GDOP, and secondly, between the entries of a measurement matrix and the GDOP, both without inverting a matrix. Consequently, two sets of entries and two sets of output variables, respectively, are used that in total yield four types of mapping architectures. Simulation results from these four architectures are presented. The performance and computational benefit of neural network-based GDOP approximation are explored.
Relation: 55(1) pp.97-108
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/50060
Appears in Collections:[通訊與導航工程學系] 期刊論文

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