Abstract
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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Azami, H., Mosavi, MR. & Sanei, S. Classification of GPS Satellites Using Improved Back Propagation Training Algorithms. Wireless Pers Commun 71, 789–803 (2013). https://doi.org/10.1007/s11277-012-0844-7
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DOI: https://doi.org/10.1007/s11277-012-0844-7