@article { author = {Amiri-Simkooei, Alireza}, title = {On the use of two L1 norm minimization methods in geodetic networks}, journal = {Earth Observation and Geomatics Engineering}, volume = {2}, number = {1}, pages = {1-8}, year = {2018}, publisher = {University of Tehran}, issn = {2588-4352}, eissn = {2588-4360}, doi = {10.22059/eoge.2018.256034.1021}, abstract = {L1 norm adjustment is a powerful technique to detect gross errors in geodetic observations. This paperinvestigates 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 rankdeficient linear(ized) system of equations. Then, an alternative technique is explained based on the leastsquares residuals. The results are tested on both linear and non-linear functional models, which confirm theefficiency of both formulations. The results also indicate that the L1 norm minimization, compared to theweighted 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 normminimization method.}, keywords = {L1 norm minimization,Data snooping procedure,Linear programming problem}, url = {https://eoge.ut.ac.ir/article_66945.html}, eprint = {https://eoge.ut.ac.ir/article_66945_30a9f86bc9c7449f56c187dc57bb4494.pdf} }