Comparison of L1 norm and L2 norm minimisation methods in trigonometric levelling networks

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Inal C., Yetkin M., Bulbul S., Bilgen B.

Tehnicki Vjesnik, vol.25, pp.216-221, 2018 (SCI-Expanded) identifier

  • Publication Type: Article / Review
  • Volume: 25
  • Publication Date: 2018
  • Doi Number: 10.17559/tv-20160809163639
  • Journal Name: Tehnicki Vjesnik
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.216-221
  • Keywords: Linear programming, Measurements with gross error, Simplex method, Trigonometric levelling networks
  • Ankara Haci Bayram Veli University Affiliated: No

Abstract

© 2018, Strojarski Facultet. All rights reserved.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.