Global Optimization Solution of Robust Estimation

Abstract

Robust estimation has proved to be a valuable approach to adjust a surveying network when there are systematic or gross errors in the observations or systematic errors in the functional model. In the present paper we propose to solve robust estimation as a global optimization problem. In particular, we will apply the simulated annealing method and genetic algorithms. The usual strategy of iteratively reweighed least squares is analyzed versus the global optimization approach. Results show that in problematic cases robust estimation is not truly robust unless performed by a global optimization method.