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Kalman filtering is a commonly used filtering algorithm in current dynamic target tracking. It is of great significance to study its dynamic tracking accuracy for military guidance, traffic navigation and other fields. Three kinds of gross errors including outliers, biases and drifts, are considered in the observations of dynamic target tracking system. Based on the traditional Extended Kalman Filter (EKF) framework, a methodology of gross error detection and compensation realizes the accurate identification and compensation of the gross errors in the observations. So that the modified EKF can effectively eliminate the gross errors in the observations by combining the system equations and the results of state estimation is closer to the true values. After simulations and comparisons, the modified EKF algorithm achieves accurate tracking of dynamic targets by effectively eliminating the influence under the condition of different types of gross errors in the observations. It can greatly improve the accuracy of dynamic target tracking.