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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/28202

Title: Neural network-based GPS GDOP approximation and classification
Authors: Dah-Jing Jwo;Chien-Cheng Lai
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程學系
Keywords: GPS;GDOP;Neural networks;Approximation;Classification
Date: 2007-01-01
Issue Date: 2011-10-21T02:36:12Z
Publisher: GPS Solutions
Abstract: abstract:In this paper, the neural network (NN)-based navigation satellite subset selection is presented. The approach is based on approximation or classification of the satellite geometry dilution of precision (GDOP) factors utilizing the NN approach. Without matrix inversion required, the NN-based approach is capable of evaluating all subsets of satellites and hence reduces the computational burden. This would enable the use of a high-integrity navigation solution without the delay required for many matrix inversions. For overcoming the problem of slow learning in the BPNN, three other NNs that feature very fast learning speed, including the optimal interpolative (OI) Net, probabilistic neural network (PNN) and general regression neural network (GRNN), are employed. The network performance and computational expense on NN-based GDOP approximation and classification are explored. All the networks are able to provide sufficiently good accuracy, given enough time (for BPNN) or enough training data (for the other three networks).
Relation: 11(1), pp.51-60
URI: http://ntour.ntou.edu.tw/handle/987654321/28202
Appears in Collections:[通訊與導航工程學系] 期刊論文

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