基于UKF的闪烁噪声机动目标跟踪

闪烁噪声下的机动目标跟踪是一个非线性非高斯系统滤波问题,传统的卡尔曼理论很难保证其跟踪精度.文中提出了一种基于UKF的闪烁噪声机动目标跟踪算法,首先对目标系统的状态方程进行无味变换,然后再进行滤波估计,以减小跟踪误差.UKF不需要求导,它能比EKF更好地迫近目标运动模型的非线性特性,具有更高的估计精度,计算量却与EKF同阶.在仿真实验中采用"协同转弯模型"作为机动目标的运动模型,雷达的量测方程也是非线性的,分别应用UKF和EKF跟踪闪烁噪声下的机动目标,结果表明,UKF能够较好地解决闪烁噪声下跟踪机动目标的难题,其跟踪精度要远远高于EKF.

Unscented Kalman Filter for Maneuver Target Tracking under Glint Noise

Traditional Kalman Filter can not guarantee tracking precision of maneuver target under glint noise because it is an nonlinear non-Gaussian problem.Unscented Kalman Filter(UKF)algorithm for maneuver target tracking under glint noise is proposed in this paper,filtering estimation is carried out after unscented transform of state equation of target system,so that tracking error is decreased.Without need of derivation,UKF can approximate non-linearity of dynamic model of target better than EKF resulting in higher estimation precision,while its computation complexity is equivalent to that of EKF.In simulation experiment,"Coordinated Turns" model is taken as dynamic model of target,and the measuring equation is also nonlinear,UKF and EKF are respectively adopted to track maneuver target under glint noise.Simulation results indicate that UKF has quite higher tracking precision than EKF.