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Optimization of satellite combination in kinematic positioning mode with the aid of genetic algorithm

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The basis of high precision relative positioning is the use of carrier phase measurements. Data differencing techniques are one of the keys to achieving high precision positioning results as they can significantly reduce a variety of errors or biases in the observations and models. Since GPS observations are usually contaminated by many errors such as the atmospheric biases, the receiver clock bias, the satellite clock bias, and so on, it is impossible to model all systematic errors in the functional model. Although the data differencing techniques are widely used for constructing the functional model, some un-modeled systematic biases still remain in the GPS observations following such differencing. Another key to achieving high precision positioning results is to fix the initial carrier phase ambiguities to their theoretical integer values. To obtain a high percentage of successful ambiguity-fixed rates, noisy GPS satellites have to be identified and removed from the data processing step. This paper introduces a new method using genetic algorithm (GA) to optimize the best combination of GPS satellites which yields the highest number of successful ambiguity-fixed solutions in kinematic positioning mode. The results indicate that the use of GA can produce higher number of ambiguity-fixed solutions than the standard data processing technique.
Rocznik
Strony
35--46
Opis fizyczny
Bibliogr. 14 poz., rys., tab.
Twórcy
  • Department of Survey Engineering, Faculty of Engineering, Chulalongkorn University, Thailand
autor
  • Department of Survey Engineering, Faculty of Engineering, Chulalongkorn University, Thailand
autor
  • Department of Civil & Geomatics Engineering, California State University, Fresno, California, U.S.A.
autor
  • Department of Civil Engineering, Changwon National University, Rep.of Korea
Bibliografia
  • Allen, F., and Karjalainen, R. (1999) Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics 51, pp. 245-271.
  • Buckland, M. (2002) AI Technique for Game Programming. The Premier Press Game Development Series, Premier Press.
  • Holland, J. H. (1975) Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor.
  • Kim, S., Ito, K., Yoshihara, D., and Wakisaka, T. (2005) Application of a Genetic Algorithm to the Optimization of Rate Constants in Chemical Kinetic Models for Combustion Simulation of HCCI Engines. JSME International Journal Series B, 48(4), pp.717-724.
  • Liu, Z., Du, Z., Zou, R. (2007) Application of the Improved Genetic Algorithms With Real Code on GPS Data Processing. International Conference on Natural Computation, pp. 420-424.
  • Liu, Z., Xiong, W., Kang, Z., Zhang, H., Qu, M. (2010) GPS Ambiguity Resolution of Single Epoch Data Using genetic algorithms. International Conference on Natural Computation, pp. 2365-2368.
  • Melanie, M. (1999) An Introduction to Genetic Algorithms(Complex Adaptive Systems), 5th ed., MIT Press.
  • Mosavi, M. R., and Divband, M. (2010) Calculation of Geometric Dilution of Precision using Adaptive Filtering Technique based on Evolutionary Algorithms. IEEE International Conference on Electrical and Control Engineering, pp.4842-4845.
  • Ran, Y., Xiong, G., Li, S., and Ye, L. (2010) Study on deformation prediction of landslide based on genetic algorithm and improved BP neural network. Kybernetes, 39(8), pp.1245-1254.
  • Stomeo, E., Kalgonova, T., and Lambert, C. (2006) A Novel Genetic Algorithm for Evolvable Hardware. IEEE Congress on Evolutionary Computation, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada.
  • Takasu, T. (2011) RTKLIB: An Open Source Program Package for GNSS Positioning. http://www.rtklib.com. Accessed 08 February 2012.
  • Wu, C., Chou, H., and Su, W. (2007) A Genetic Approach for Coordinate Transformation Test of GPS Positioning. IEEE, Geoscience And Remote Sensing Letters, 4(2), pp.297-301.
  • Xu, J., Arslan, T., Wang, Q., and Wan, D. (2002) An EHW Architecture for Real-Time GPS Attitude Determination Based on Parallel Genetic Algorithm. Proceedings of the 2002 NASA/DOD Conference on Evolvable Hardware, VA, USA.
  • Yeniay, Ö. (2005) Penalty Function Method for Constrained Optimization with Genetic Algorithms. Mathematical and Computational Applications, 10(1), pp. 45-56.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fce1170a-d674-460f-b094-82d18f12d1ec
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