Solving the local positioning problem using a four-layer artificial neural network
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Abstract:
Today, the global positioning systems (GPS) do not work well in buildings and in dense urban areas when there is no lines of sight between the user and their satellites. Hence, the local positioning system (LPS) has been considerably used in recent years. The main purpose of this research is to provide a four-layer artificial neural network based on nonlinear system solver (NLANN) for local positioning problem. To evaluate the performance of artificial neural network, three methods of gauss-newton (GN), genetic algorithm (GA) and hybrid particle swarm optimization (HPSO) have been used. The results indicate that the proposed model has high accuracy. The accuracy of the artificial neural network on the simulated data is 0.05 m, while the best accuracy in other algorithms is about 0.45 meters. In the data of Italy's GPS network, the artificial neural network has been reached to accuracy below 10 cm in one minute. Also, artificial neural network has better accuracy in different dimensions of study area and different signal to noise ratio (SNR), and by increasing the number of stations, it has achieved good results in less time. Whereas other algorithms have not get well accuracy. However, the HPSO has better results related to GA and GN algorithms.
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Journal title
volume 7 issue 4
pages 21- 40
publication date 2020-03
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