Discovering approximate expressions of GPS geometric dilution of precision using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Wu2012332,
  author =       "Chih-Hung Wu and Ya-Wei Ho and Li-Wun Chen and 
                 You-Dong Huang",
  title =        "Discovering approximate expressions of GPS geometric
                 dilution of precision using genetic programming",
  journal =      "Advances in Engineering Software",
  volume =       "45",
  number =       "1",
  pages =        "332--340",
  year =         "2012",
  ISSN =         "0965-9978",
  DOI =          "doi:10.1016/j.advengsoft.2011.10.013",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0965997811002894",
  keywords =     "genetic algorithms, genetic programming, Global
                 Positioning System (GPS), Geometric Dilution of
                 Precision (GDOP), Regression, White-boxed methods, Soft
                 computing",
  abstract =     "Global Positioning System (GPS) has been used
                 extensively in various fields. Geometric Dilution of
                 Precision (GDOP) is an indicator showing how well the
                 constellation of GPS satellites is organised
                 geometrically, so as a reliability indicator presenting
                 the GPS positioning accuracy. Traditional methods for
                 calculating GPS GDOP need to solve the measurement
                 equations where involve complicated matrix
                 transformation and inversion. Some studies rephrase the
                 calculation of GPS GDOP a regression problem and employ
                 black-box machine learning methods for problem solving.
                 However, the regression models obtained from such
                 methods lack of expressivity for describing the
                 relationships among variables. Making the structures of
                 GDOP expressions visible is valuable because they can
                 be further studied or tailored for specific GPS
                 applications. This study employs the technique of
                 genetic programming (GP) for the regression of GPS
                 GDOP. The performance of GP working with various
                 operators and parameter settings is studied and
                 discussed. The experimental results show that GP
                 generates precise models with better expressivity for
                 GPS GDOP than other methods.",
}

Genetic Programming entries for Chih-Hung Wu Ya-Wei Ho Li-Wun Chen You-Dong Huang

Citations