Volume 13, Issue 3 (September 2017)                   IJEEE 2017, 13(3): 219-227 | Back to browse issues page


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Mosavi M R, Rashidinia A. Improving Accuracy of DGPS Correction Prediction in Position Domain using Radial Basis Function Neural Network Trained by PSO Algorithm. IJEEE 2017; 13 (3) :219-227
URL: http://ijeee.iust.ac.ir/article-1-1070-en.html
Abstract:   (5450 Views)

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Function (RBF) has been developed. In many previous works all parameter of RBF NN are optimizing by evolutionary algorithm such as Particle Swarm Optimization (PSO), but in our approach shape parameter and centers of RBF NN are calculated in better way, in addition, search space for PSO algorithm will be reduced which cause more accurate and faster approach. The obtained results show that RMS has been reduced about 0.13 meter. Moreover, results are tabulated in the tables which verify the accuracy and faster convergence nature of our approach in both on-line and off-line training methods.

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Type of Study: Research Paper | Subject: Satellite Communications
Received: 2017/03/26 | Revised: 2017/12/09 | Accepted: 2017/07/20

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.