On the use of two L1 norm minimization methods in geodetic networks | ||
| Earth Observation and Geomatics Engineering | ||
| مقاله 1، دوره 2، شماره 1، شهریور 2018، صفحه 1-8 اصل مقاله (598.8 K) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2018.256034.1021 | ||
| نویسنده | ||
| Alireza Amiri-Simkooei* | ||
| Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran | ||
| چکیده | ||
| L1 norm adjustment is a powerful technique to detect gross errors in geodetic observations. This paper investigates the results of two formulations that provide the L1 norm adjustment of a linear functional model. The usual method for implementation of the L1 norm adjustment leads to solving a linear programming (LP) problem. The formulation of the L1 norm minimization is presented based on the LP problem for a rank deficient linear(ized) system of equations. Then, an alternative technique is explained based on the least squares residuals. The results are tested on both linear and non-linear functional models, which confirm the efficiency of both formulations. The results also indicate that the L1 norm minimization, compared to the weighted least squares method, is a robust technique for the detection of blunders in geodetic observations. Finally, this contribution presents a data snooping procedure to the residuals obtained by the L1 norm minimization method. | ||
| کلیدواژهها | ||
| L1 norm minimization؛ Data snooping procedure؛ Linear programming problem | ||
| مراجع | ||
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