It is well known to the Kalman filter design and estimation community that the values for the process noise, Q, and measurement noise, R, covariance matrices primarily dictate the filter performance. In addition, selecting proper values for Q and R is traditionally done in an ad-hoc manner. This paper provides a new look into the roles of the process noise and measurement noise matrices using the spacecraft attitude estimation problem as the design benchmark. This includes an interesting situation where the theoretical values of Q and R, derived as a function of gyro and star tracker noise parameters, are exactly matched with the noise characteristics employed on the sensor model side. However, the filter still exhibits poor attitude estimation performance, as measured against an attitude knowledge requirement, while subject to a high rate slew profile. A simulation based tuning methodology is developed to optimize the filter performance and bring the attitude estimation back to within the required attitude knowledge bound.