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Journals / Harita Dergisi / 2010 / Cilt: 76 - Sayı: 143

($L _1$ Norm mimimization in geodetic networks: the case of levelling network)

Jeodezik ağlarda $L _1$ norm minimizasyonu: yükseklik ağı örneği

MEVLÜT YETKİN
CEVAT İNAL
Pages
13–18
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results show that Li norm minimization is a more successful method than the classical least squares method in outlier detection and parameter estimation with contaminated data.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Lı norm minimization is a robust technique to detect outliers in geodetic networks.
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Key Findings & Data:Li norm adjustment can be implemented by solving a linear programming problem.
💡
Conclusion & Impact:The results show that Li norm minimization is a more successful method than the classical least squares method in outlier detection and parameter estimation with contaminated data.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Lı norm minimization is a robust technique to detect outliers in geodetic networks. Li norm adjustment can be implemented by solving a linear programming problem. In this paper, the formulation of the Li norm minimization method in'a rank deficient Gauss-Markov model is studied. The method has been tested in an artificially structured levelling network. The results show that Li norm minimization is a more successful method than the classical least squares method in outlier detection and parameter estimation with contaminated data.

Özet

Lı norm minimizasyonu jeodezik ağlarda uyuşumsuz ölçüleri belirlemek için kullanılan robust bir tekniktir. Lı norm dengelemesi bir lineer programlama probleminin çözülmesiyle gerçekleştirilebilir. Bu makalede, rank defektli bir Gauss-Markov modelinde Lı norm minimizasyonu yönteminin formülasyonu incelenmiştir. Yöntem yapay olarak oluşturulan bir yükseklik ağında test edilmiştir. Test sonuçları bu robust dengeleme tekniğinin uyuşumsuz ölçü belirlemede ve kirletilmiş ölçü setiyle parametre kestiriminde klasik en küçük kareler yönteminden daha başarılı olduğunu göstermektedir.

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Cite This Article

YETKİN, M., İNAL, C. (2010). ($L _1$ Norm mimimization in geodetic networks: the case of levelling network). Harita Dergisi, Cilt: 76 - Sayı: 143, 13–18. https://ajindex.com/articles/175524

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