碩士 / 國立海洋大學 / 航運技術研究所 / 89 / For the GPS navigation and positioning, in order to improve satellite geometry and then improve accuracy, it is desirable to use all of the signals of the satellites in view except those with too low elevation angles. Because of the geometric relationships between the receiver position and the satellite positions, certain satellites are actually not effective in raising the whole positioning accuracy. It is, nevertheless, very time-consuming in the positioning process. Four satellites or more will generally be required for GPS position fix. Some receiver hardware may be limited to processing limited number of visible satellites. Therefore, it is sometimes necessary to select the optimal satellite subset.
Geometry Dilution of Precision (GDOP) is an indicator of the quality of the geometry of the satellite constellation. It will be viewed as the multiplicative factor that magnifies ranging error. A smaller GDOP indicates that the geometry is better, which yield a better positioning accuracy. Matrix inversion will be required for computing GDOP. The GDOP will reach a minimum value when using all satellites in view, however it is very time-consuming especially when the number of satellites is large. Besides, the addition of satellites will not all raise accuracy effectively. In this paper, the application of backpropagation neural networks (BPNN) to GPS satellite GDOP approximation is presented. The BPNN can handle the non-linear mapping to avoid matrix inversion and choose the satellite subset that minimizes the GDOP. The proposed algorithms for GDOP approximation will provide an efficient alternative method for optimal satellite subset selection.
| Identifer | oai:union.ndltd.org:TW/089NTOU0300005 |
| Date | January 2001 |
| Creators | Guo-Bin Jin, 金國斌 |
| Contributors | Dah-Jing Jwo, 卓大靖 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 57 |
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