چكيده لاتين :
In this study, an intelligent approach is applied to reset the error covariance matrix of Kalman filter (KF) for high maneuver target tracking. In practice, the standard KF is used for non-maneuvering target tracking
applications, which is optimal in the Minimum Mean Square Error (MMSE) sense. Furthermore, it has fast convergence rate. However, after some iterations the steps of the KF become very small. Because of small steps
in KF, the accuracy of target tracking may be seriously degraded in presence of maneuver. This drawback can
be overcome by resetting the error covariance matrix of the KF. Since the information of earlier updates will be
partially lost by resetting the error covariance matrix, system should reset it just when the target maneuvers and
KF steps are not large enough to track the target accurately. Moreover, resetting factor should be proportional
to the maneuver. Therefore, we present an intelligent approach based on target maneuver detection to determine
proper instants for resetting the error covariance matrix. In addition, the new scheme is enable to determine the
optimal value ofresetting factor in each iteration effectively. Simulation results illustrate that the tracking ability
ofthe proposed scheme is more than conventional approaches, especially for high maneuvering target tracking
applications.
