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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2014

01.08.2014

Rapid and Precise GLONASS GDOP Approximation using Neural Networks

verfasst von: Amir Tabatabaei, Mohammad Reza Mosavi

Erschienen in: Wireless Personal Communications | Ausgabe 4/2014

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Abstract

Russian global navigation satellite system (GLONASS) provides civilian and military users three-dimensional position determination and navigation services as same as US global positioning system. Geometric dilution of precision (GDOP) provides a simple interpretation of positioning precision. Usual method for GLONASS GDOP calculation is matrix inversion. However this process imposes a huge calculation load on receiver, especially when large number of visible satellites exists. To overcome this problem, artificial neural network is used. Different configurations and training methods are simulated on a data base obtained by a GLONASS receiver. Then navigation precision and execution times are explored and compared. Results show that recurrent neural network has 0.00024 RMS error, which is the best against other focused tools including feed forward back propagation and radial basis function neural network with usual training and with genetic algorithm adopted weights and biases.
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Metadaten
Titel
Rapid and Precise GLONASS GDOP Approximation using Neural Networks
verfasst von
Amir Tabatabaei
Mohammad Reza Mosavi
Publikationsdatum
01.08.2014
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2014
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-014-1660-z

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