Formulation of L1 Norm Minimization in Gauss-Markov Models

Abstract

L1 norm minimization adjustment is a technique to detect outlier observations in geodetic networks. The usual method for implementation of L1 norm adjustment leads to the solving of a linear programming problem. In this paper, the formulation of the L1 norm minimization for a rank deficient Gauss-Markov model will be presented. The results have been tested on both linear and non-linear models, which confirm the efficiency of the suggested formulation.