칼만필터를 이용한 추적레이터의 추적 성능향상에 관한 연구

Abstract

Although the Linear Kalman Filter is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability, and robustness, the application of the Linear Kalman Filter to nonlinear systems can be difficult. For solving this problem, several researchers improved the existing methods. The most common approach is to use the Extended Kalman Filter which simply linearizes all nonlinear models, so that the traditional linear Kalman filter can be applied. But more errors result from variations and linearization of the target motion state in maneuvering, which may seriously degrade the performance of the Extended Kalman Filter or even cause the filter to diverge. Therefore, a novel approach based on adaptive variable Kalman Filter structure is proposed in this paper, which can adjust Kalman Filter model to the changes of target motion state in maneuvering. In the proposed approach, when a target signal quality is determined by proposed filter algorithm, the process noise and measurement noise covariance model will be adjusted with varied target motion state. The proposed approach is verified by using the Test flight in Naro space center. Also the performances of this target tracking algorithm are evaluated by using the simulations of a real rocket trajectory.