Inal, CevatYetkin, MevlutBulbul, SercanBilgen, Burhaneddin2020-03-262020-03-2620181330-36511848-6339https://dx.doi.org/10.17559/TV-20160809163639https://hdl.handle.net/20.500.12395/36430The most widely-used parameter estimation method today is the L-2 norm minimisation method known as the Least Squares Method (LSM). The solution to the L-2 norm minimisation method is always unique and is easily computed. This method distributes errors and is sensitive to outlying measurements. Therefore, a robust technique known as the Least Absolute Values Method (LAVM) might be used for the detection of outliers and for the estimation of parameters. In this paper, the formulation of the L-1 norm minimisation method will be explained and the success of the method in the detection of gross errors will be investigated in a trigonometric levelling network.en10.17559/TV-20160809163639info:eu-repo/semantics/openAccesslinear programmingmeasurements with gross errorsimplex methodtrigonometric levelling networksReview25216221Q3WOS:000433290300031Q4