We mathematically analyze the correlations that arise between measurement parameters. This is done by understanding the geometrical transformations that a data point undergoes when correlations are determined between normally distributed measurement parameters. We use this understanding to develop a new algorithm for the discrete Kalman Filter. The analysis and methodology adopted in this work can be extended to the derivatives of Kalman Filter, resulting in similar improvements. The effectiveness of this method is verified through simulations of mobile robot mapping problem with an Extended Kalman Filter and the results are presented. © Springer Nature Singapore Pte Ltd. 2019.