The Kalman Filter (KF) is the most popular approach in sensor fusion and navigation applications. Improving the accuracy of estimation may increase the navigation accuracy. KF is the best choice for minimum variance optimal estimation, but it is not the best approach to improve some of the main and classic features such as steady-state error reduction. These features are more important than optimality for applications like accuracy improvement in navigation. Also, these features and their solutions have been addressed in classic control techniques as a widely known subject. Therefore, some similar improvements in the estimation problem are discussed in the literature. These studies try to conserve the optimality in the covariance of the error, but classic and optimal features cannot be achieved simultaneously. Hence, these methods are not as efficient as expected. This paper intends to improve the estimation of position and velocity in navigation problems by integrating classic and modern control techniques. Beside the classic features and advantages, it may be seen that the resulted covariance of error is not minimum anymore, but it is comparable with the optimal methods.

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