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, cilt.25, ss.216-221, 2018 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 25
  • Basım Tarihi: 2018
  • Doi Numarası: 10.17559/tv-20160809163639
  • Dergi Adı: Tehnicki Vjesnik
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.216-221
  • Anahtar Kelimeler: Linear programming, Measurements with gross error, Simplex method, Trigonometric levelling networks
  • Ankara Hacı Bayram Veli Üniversitesi Adresli: Hayır

Özet

© 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.