A Study on GPS GDOP Approximation Using Support-Vector Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Wu:2011:ieeeTIM,

• author = "Chih-Hung Wu and Wei-Han Su and Ya-Wei Ho",
• title = "A Study on GPS GDOP Approximation Using Support-Vector Machines",
• journal = "IEEE Transactions on Instrumentation and Measurement",
• year = "2011",
• month = jan,
• volume = "60",
• number = "1",
• pages = "137--145",
• abstract = "Global Positioning System (GPS) has extensively been used in various fields. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is geometrically organised. GPS positioning with a low GDOP value usually gains better accuracy. However, the calculation of GDOP is a time- and power-consuming task that involves complicated transformation and inversion of measurement matrices. When selecting from many GPS constellations the one with the lowest GDOP for positioning, methods that can fast and accurately obtain GPS GDOP are imperative. Previous studies have shown that numerical regression on GPS GDOP can get satisfactory results and save many calculation steps. This paper deals with the approximation of GPS GDOP using statistics and machine learning methods. The technique of support vector machines (SVMs) is mainly focused. This study compares the performance of several methods, such as linear regression, pace regression, isotonic regression, SVM, artificial neural networks, and genetic programming (GP). The experimental results show that SVM and GP have better performance than others.",
• keywords = "genetic algorithms, genetic programming, GPS GDOP approximation, SVM, artificial neural networks, geometric dilution of precision, isotonic regression, linear regression, pace regression, support-vector machines, Global Positioning System, learning (artificial intelligence), neural nets, support vector machines",
• DOI = "doi:10.1109/TIM.2010.2049228",
• ISSN = "0018-9456",
• notes = "Also known as \cite{5467147}",

}

Genetic Programming entries for Chih-Hung Wu Wei-Han Su Ya-Wei Ho

Citations