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针对强机动目标跟踪问题,基于当前统计(CS,Current Statistical)模型、改进输入估计(MIE,Modified Input Estimation)和无迹强跟踪滤波器,提出了一种新的自适应目标跟踪算法.该算法引入Jerk输入估计改进了当前统计模型的状态方程和机动加速度方差调整方法,利用改进的无迹强跟踪滤波器实现了状态协方差、状态噪声协方差和机动频率的联合自适应.在没有加速度先验知识的情况下,能够实时准确跟踪目标连续强机动、匀加速机动和匀速运动状态.仿真实验表明:相比CS模型无迹滤波算法、CS模型无迹强跟踪算法和交互多模型算法,该算法在对目标强机动的适应性、跟踪精度和对突变状态跟踪的收敛性方面都有更好的性能.
Aiming at the problem of strong maneuvering target tracking, a new adaptive target tracking algorithm is proposed based on the current statistical (CS) model, the Modified Input Estimation (MIE), and the traceless tracking filter. The introduction of Jerk input estimation improves the state equation and the method of adjusting the maneuvering acceleration variance of the current statistical model, and the joint adaptation of state covariance, state noise covariance and maneuver frequency is achieved by using the improved traceless tracking filter. Without acceleration Under the condition of knowing knowledge, the target can be continuously and accurately tracked in a continuous, strong and uniform manner. Simulation results show that compared with the unscented CS algorithm, the CS model has no tracking algorithm and an interactive multi-model algorithm The algorithm has better performance in adaptability to target strong maneuvering, tracking accuracy and convergence of catastrophe state tracking.