自适应高斯模型与多基地雷达机动目标跟踪

Adaptive Gauss Model and Maneuvering Target Tracking in Multi-static Radar System

为改善多基地雷达系统对高机动目标的跟踪性能,提出了基于自适应高斯模型和扩展卡尔曼滤波(EKF)的机动目标跟踪算法。将目标加速度的概率密度特性描述为一具有均值和方差的高斯分布,建立了系统的离散状态方程和非线性观测方程进行EKF滤波,并在每个采样周期实现对输入控制信号及过程噪声协方差的更新,使加速度符合目标的实际变化情况。MonteCarlo仿真结果显示,对目标的变加速轨迹,该算法在位置和加速度上的均方根误差均比Singer模型小,且误差曲线平滑,表明了该算法能对机动目标实现准确跟踪。

Maneuvering target tracking algorithm based on adaptive Gauss model and EKF was built for improving the tracking performance in Multi-static systems. Probability density speciality of acceleration was described as a Gauss distributing, and disperse state equation and nonlinear observation equation were acquired to realize EKF, it is important that updating input control and course noise covariance on each step accords with the target＇s actual motion. Monte Carlo simulation indicates RMS of location and acceleration provided by the algorithm are less than Singer model and error curve is more smooth, accordingly the accurate tracking to the maneuvering target is realized.