Classification of GPS Satellites Using Improved Back Propagation Training Algorithms

Factor geometric dilution of precision (GDOP) is an indicator that shows the quality of GPS positioning and has often been used for choosing suitable satellite’s subset from at least 24 orbited existing satellites. The calculation of GPS GDOP is a time-consuming task which can be done by solving mea...

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Bibliographic Details
Published in:Wireless personal communications Vol. 71; no. 2; pp. 789 - 803
Main Authors: Azami, Hamed, Mosavi, Mohammad-Reza, Sanei, Saeid
Format: Journal Article
Language:English
Published: Boston Springer US 01.07.2013
Springer
Subjects:
Accuracy
Algorithms
Applied sciences
Artificial intelligence
Back propagation
Classification
Communications Engineering
Computer Communication Networks
Computer science; control theory; systems
Connectionism. Neural networks
Engineering
Exact sciences and technology
Global Positioning System
Learning theory
Mathematical analysis
Networks
Neural networks
Signal,Image and Speech Processing
GPS GDOP
Artificial neural network
Basic back propagation
Classification
Resilient back propagation
Conjugate gradient algorithm
Conjugate gradient method
Backpropagation
Positioning
Gradient
GPS system
Orbit
Satellite
Neural network
Modeling
Adaptive method
Search algorithm
Backpropagation algorithm
Matrix inversion
ISSN:0929-6212, 1572-834X
Online Access:Get full text
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Summary:Factor geometric dilution of precision (GDOP) is an indicator that shows the quality of GPS positioning and has often been used for choosing suitable satellite’s subset from at least 24 orbited existing satellites. The calculation of GPS GDOP is a time-consuming task which can be done by solving measurement equations with complicated matrix transformation and inversion. In order to decrease this computational burden, in this research the artificial neural network (ANN) has been used. Although the basic back propagation (BP) is the most popular ANN algorithm and can be used in the estimators, detectors or classifiers, it is too slow for practical problems and its performance is not satisfactory in many cases. To overcome this problem, six algorithms, namely, BP with adaptive learning rate and momentum, Fletcher-Reeves conjugate gradient algorithm (CGA), Polak–Ribikre CGA, Powell–Beale CGA, scaled CGA, and resilient BP have been proposed to reduce the convergence time of the basic BP. The simulation results show that resilient BP, compared with other methods, has greater accuracy and calculation time. The resilient BP can improve the classification accuracy from 93.16 to 98.02 % accuracy by using the GPS GDOP measurement data.
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ISSN:0929-6212
1572-834X
DOI:10.1007/s11277-012-0844-7