To improve the performance of Converted Measurement Kalman Filter (CMKF), a more accurate evaluation method for converted measurement error statistics (means and covariance) was presented. There were two sets of information that could be used to evaluate the converted measurement error statistics, i.e., measurements of the target and a priori state estimate of the filter. Firstly, the proposed procedure in this work derived the means and covariance of the filter’s a priori spherical state estimation errors. Secondly, a more accurate evaluation of the converted measurement error statistics was obtained according to the a priori spherical state estimate of the filter instead of measurements. Finally, the improved evaluation method for converted measurement error statistics was utilized to implement the CMKF algorithm for a target tracking scenario. The simulation results show that the proposed method can provide superior performance in terms of convergence and estimation accuracy, especially in the case of significant measurement noises.