Kalman ﬁlter (KF) is a widely used navigation algorithm, especially for precise positioning applications. However, the exact ﬁlter parameters must be deﬁned a priori to use standard Kalman ﬁlters for coping with low error values. But for the dynamic system model, the covariance of process noise is a priori entirely undeﬁned, which results in diﬃculties and challenges in the implementation of the conventional Kalman ﬁlter. Kalman Filter with recursive covariance estimation applied to solve those complicated functional issues, which can also be used in many other applications involving Kalaman ﬁltering technology, a modiﬁed Kalman ﬁlter called MKF-RCE. While this is a better approach, KF with SAR tuned covariance has been proposed to resolve the problem of estimation for the dynamic model. The data collected at (x: 706970.9093 m, y: 6035941.0226 m, z: 1930009.5821 m) used to illustrate the performance analysis of KF with recursive covariance and KF with computational intelligence correction by means of SAR (Search and Rescue) tuned covariance, when the covariance matrices of process and measurement noises are completely unknown in advance.