Proceeding of the Joint International Symposium on Deformatuon Monitoring. 2-4 November, 2011. Hong Kong. China. Report. Kalman filtering is a multiple-input, multiple-output filter that can optimally estimate the states of a system, so it can be considered a suitable means for deformation analysis. The states are all the variables needed to completely describe the system behavior of the deformation process as a function of time (such as position, velocity etc.). The standard Kalman filter estimates the state vector where the measuring process is described by a linear system. While, in order to process a non-linear system an optimized aspect of Kalman filter is appropriate. The main purpose of this research is to evaluate the Optimized Kalman filter as a non-robust method versus the IWST (Iterative Weighted Similarity Transformation) as a rigorous (also called robust) method. To satisfy this objective, first a detailed description on executing the Extended Kalman filter using the observation of angles and distances directly is provided. Later on, five sets of 2-D Total Station data include distances and angles are used to demonstrate the Optimized Kalman Filter. For detecting the deformation, single point test for every point is applied component by component as a local test. Later on, the findings from Optimized Kalman Filter are compared and evaluated against the results from IWST testing. In general, the outcome of Kalman filter algorithm is close to the preliminary results from IWST testing. The maximum and minimum differences in computed displacements are equal to 0.0002 and 0.002 in meters respectively. Finally, Kalman filter approaches, having some properties, are recognized as suitable techniques for deformation analysis.