A Study on GPS GDOP Approximation Using Support-Vector Machines

摘要: Global Positioning System (GPS) has extensively been used in various fields. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation GPS satellites geometrically organized. positioning with a low GDOP value usually gains better accuracy. However, calculation time- and power-consuming task that involves complicated transformation inversion measurement matrices. When selecting from many constellations one lowest for positioning, methods can fast accurately obtain are imperative. Previous studies have shown numerical regression on get satisfactory results save steps. This paper deals approximation using statistics machine learning methods. The technique support vector machines (SVMs) mainly focused. study compares performance several methods, such as linear regression, pace isotonic SVM, artificial neural networks, genetic programming (GP). experimental show SVM GP than others.