| Summary: | 碩士 === 國立政治大學 === 地政研究所 === 99 === The conventional single point positioning using GPS pseudo rangemeasurements, are vulnerable to ionospheric errors, leading to poor positioningaccuracy. Constructing a real-time ionospheric model is one of the methods that
can reduce the ionospheric errors and improve the single point positioning accuracy.
Although there are many methods to construct regional ionosphere model,using artificial neural network (ANN) to construct a real-time ionospheric model is less to be mentioned. This study used back-propagation artificial neural network to estimate a regional real-time ionospheric model by selecting the appropriate training functions and the number of hidden layers and its’ nodes. The neural network had to be ‘trained’ by the computed TECs from reference stations’ duel-frequency GPS data until the required accuracy was achieved.
The experimental data are collected from 6 e-GPS stations of Tainan city government on January 3 to January 5, 2008. The input values for the ANN includ the geographical location of the ionosphere pierce point (IPP) and solar activity (sunspot number). The output value are those IPPs’ vertical total electron content (VTEC). Different times range and data types (IPPs’ or raster
data) for the impact of the ANN are tested. And then compared to Klobuchar model and global ionopheric model, according to the correct rate and the ΔTEC statistic table decide the effectiveness of ANN.
According to the test results, the regional ionopheric model constructed by ANN can corrected 80% of the ionospheric errors, the standard deviation of ΔTEC is less than ±3TECU.
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