A Study on GPS GDOP Approximation Using Support-Vector Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Chih-Hung Wu and Wei-Han Su and Ya-Wei Ho",
  title =        "A Study on GPS GDOP Approximation Using Support-Vector
  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
  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
  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