XML Print

Abstract:   (410 Views)
Geometric Dilution of Precision (GDOP) is a coefficient for constellations of Global Positioning System (GPS) satellites. These satellites are organized geometrically. Traditionally, GPS GDOP computation is based on the inversion matrix with complicated measurement equations. A new strategy for calculation of GPS GDOP is construction of time series problem; it employs machine learning and artificial intelligence methods for problem-solving. In this paper, the time Delay Neural Network (TDNN) is introduced to the GPS satellite DOP classification. The TDNN has a memory for archiving past event that is critical in GDOP approximation. The TDNN approach is evaluated all subsets of satellites with the less computational burden. Therefore, the use of the inverse matrix method is not required. The proposed approach is conducted for approximation or classification of the GDOP. The experiments show that the approximate total RMS error of TDNN is less than 0.00022 and total performance of satellite classification is 99.48%.
Full-Text [PDF 2800 kb]   (125 Downloads)    
  • A novel method is proposed for clustering of GPS GDOP.
  • The TDNN model is applied in the approximation of GPS GDOP.
  • Best subset and optimal group of satellite are chosen for positioning.
  • Actual navigation setup is used to testify the accuracy of the proposed method.

Type of Study: Research Paper | Subject: Signal Processing
Received: 2018/10/19 | Accepted: 2019/08/25 | Published: 2019/09/21

Creative Commons License
© 2020 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.