Formulation of Norm Minimization in Gauss-Markov Models
Publication: Journal of Surveying Engineering
Volume 129, Issue 1
Abstract
norm minimization adjustment is a technique to detect outlier observations in geodetic networks. The usual method for implementation of norm adjustment leads to the solving of a linear programming problem. In this paper, the formulation of the norm minimization for a rank deficient Gauss-Markov model will be presented. The results have been tested on both linear and non-linear models, which confirm the efficiency of the suggested formulation.
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References
Bazaraa, M. S., Jarvis, J. J., and Sherali, H. D. (1990). Linear programming and network flows, 2nd Ed., Wiley, New York.
Dantzig, G. B. (1963). Linear programming and extensions, Princeton University Press, Princeton, N.J.
Department of Surveying Engineering (1993). “Micro-geodesy network for educational purposes in Baghbahadoran.” The Univ. of Isfahan, 81744, Isfahan, Iran.
Koch, K. R. (1999). Parameter estimation and hypothesis testing in linear models, Springer, Berlin.
Marshall, J., and Bethel, J., (1996). “Basic concepts of L1 norm minimization for surveying applications.” J. Surv. Eng., 122(4), 168–179.
Mikhail, E. M. (1976). Observations and least squares, IEP-A dun-Donnelley, Chicago.
Information & Authors
Information
Published In
Journal of Surveying Engineering
Volume 129 • Issue 1 • February 2003
Pages: 37 - 43
Copyright
Copyright © 2003 American Society of Civil Engineers.
History
Received: Nov 1, 2001
Accepted: May 28, 2002
Published online: Jan 15, 2003
Published in print: Feb 2003
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