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.
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