Review article
https://doi.org/10.17559/TV-20160809163639
Comparison of L1 Norm and L2 Norm Minimisation Methods in Trigonometric Levelling Networks
Cevat Inal
orcid.org/0000-0001-8980-2074
; Selcuk University, Engineering Faculty, Department of Geomatics Engineering, 42075, Selcuklu, Konya, Turkey
Mevlut Yetkin
; Izmir Katipcelebi University, Engineering and Architecture Faculty, Department of Geomatics Engineering, 35620, Cigli, Izmir, Turkey
Sercan Bulbul
orcid.org/0000-0001-6066-611X
; Selcuk University, Engineering Faculty, Department of Geomatics Engineering, 42075, Selcuklu, Konya, Turkey
Burhaneddin Bilgen
orcid.org/0000-0002-1955-7568
; Selcuk University, Engineering Faculty, Department of Geomatics Engineering, 42075, Selcuklu, Konya, Turkey
Abstract
The most widely-used parameter estimation method today is the L2 norm minimisation method known as the Least Squares Method (LSM). The solution to the L2 norm minimisation method is always unique and is easily computed. This method distributes errors and is sensitive to outlying measurements. Therefore, a robust technique known as the Least Absolute Values Method (LAVM) might be used for the detection of outliers and for the estimation of parameters. In this paper, the formulation of the L1 norm minimisation method will be explained and the success of the method in the detection of gross errors will be investigated in a trigonometric levelling network.
Keywords
linear programming; measurements with gross error; simplex method; trigonometric levelling networks
Hrčak ID:
200621
URI
Publication date:
26.5.2018.
Visits: 3.119 *