Sensors, Vol. 19, Pages 5047: Efficacy of Msplit Estimation in Displacement Analysis (Sensors)

Sets of geodetic observations often contain groups of observations that differ from each other in the functional model (or at least in the values of its parameters). Sets of observations obtained at various measurement epochs is a practical example in such a context. From the conventional point of view, for example, in the least squares estimation, subsets in question should be separated before the parameter estimation. Another option would be application of Msplit estimation, which is based on a fundamental assumption that each observation is related to several competitive functional models. The optimal assignment of every observation to the respective functional model is automatic during the estimation process. Considering deformation analysis, each observation is assigned to several functional models, each of which is related to one measurement epoch. This paper focuses on the efficacy of the method in detecting point displacements. The research is based on example observation sets and the application of Monte Carlo simulations. The results were compared with the classical deformation analysis, which shows that the Msplit estimation seems to be an interesting alternative for conventional methods. The most promising are results obtained for disordered observation sets where the Msplit estimation reveals its natural advantage over the conventional approach.