基于改进Jerk模型的跳跃滑翔目标跟踪

Skip-glide target tracking based on improved Jerk model

针对雷达对跳跃滑翔目标跟踪问题,提出了一种基于改进Jerk模型的无迹卡尔曼滤波(UKF)跟踪算法。该方法针对常规Jerk算法需要人为预先设定加加速度方差和“急动”频率而引入的估计误差,通过当前位置估计值和当前位置一步预测值进行加加速度方差自适应计算,并将“急动”频率与加加速度方差相关联,在目标运动状态估计的同时实现了模型参数自适应调整。同时,将改进Jerk模型与UKF算法相结合,给出了整体算法流程,并进行了仿真实验。仿真结果表明,与常规Jerk模型算法相比,本文提出的方法实现了模型参数的自适应调整,在跟踪过程中更能适应目标的机动特性。

Aiming at the problem of tracking skip-glide targets using radar,this paper proposes an Unscented Kalman Filter(UKF)tracking algorithm based on the improved Jerk model.Aiming at the estimation error introduced by the artificial preset acceleration variance and Jerk frequency of the conventional Jerk algorithm,this method calculates the acceleration variance adaptively through the current position estimation value and the current position one-step prediction value,and correlates the jerk frequency with the acceleration variance,realizing the adaptive adjustment of the model parameters while estimating the target motion state.At the same time,the improved Jerk model is combined with UKF algorithm,and the overall algorithm flow is given,and simulation experiments are carried out.The simulation results show that compared with the conventional Jerk model algorithm,the method proposed in this paper realizes the adaptive adjustment of model parameters,making the tracking process more adaptable to the maneuvering characteristics of the target.