Discovering approximate expressions of GPS geometric dilution of precision using genetic programming
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- @Article{Wu2012332,
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author = "Chih-Hung Wu and Ya-Wei Ho and Li-Wun Chen and
You-Dong Huang",
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title = "Discovering approximate expressions of GPS geometric
dilution of precision using genetic programming",
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journal = "Advances in Engineering Software",
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volume = "45",
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number = "1",
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pages = "332--340",
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year = "2012",
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ISSN = "0965-9978",
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DOI = "doi:10.1016/j.advengsoft.2011.10.013",
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URL = "http://www.sciencedirect.com/science/article/pii/S0965997811002894",
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keywords = "genetic algorithms, genetic programming, Global
Positioning System (GPS), Geometric Dilution of
Precision (GDOP), Regression, White-boxed methods, Soft
computing",
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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