- Tytuł:
- Comparison of robust estimators for leveling networks in Monte Carlo simulations
- Autorzy:
- Pokarowska, M.
- Powiązania:
- https://bibliotekanauki.pl/articles/106789.pdf
- Data publikacji:
- 2016
- Wydawca:
- Politechnika Warszawska. Wydział Geodezji i Kartografii
- Tematy:
-
leveling network
robust estimation
outliers
gross error
internal reliability index
sieć niwelacyjna
estymacja
elementy odstające
błąd całkowity
wewnętrzny wskaźnik niezawodności - Opis:
- We compared the method of least squares (LS), Pope’s iterative data snooping (IDS) and Huber’s M-estimator (HU) in realistic leveling networks, for which the heights or the vertical displacements of points are known. The study was conducted using the Monte Carlo simulation, in which one repeatedly generates sets of observations related to the measurement data, then calculates values of the estimators and, finally, assesses it with respect to the real coordinates. To simulate outliers we used popular mixture models with two or more normal distributions. It is shown that for small, strong networks robust methods IDS and HU are more accurate than LS, but for large, weak networks occurring in practice there is no significant difference between the considered methods in the accuracy of the solution.
- Źródło:
-
Reports on Geodesy and Geoinformatics; 2016, 101; 70-81
2391-8365
2391-8152 - Pojawia się w:
- Reports on Geodesy and Geoinformatics
- Dostawca treści:
- Biblioteka Nauki
Artykuł