基于UT变换与卡尔曼滤波的目标跟踪研究

Study of target tracking based on improved unscented transform Kalman filtering

为了提高滤波速度和精度,将Unscented变换与卡尔曼滤波相结合,建立了Unscented卡尔曼滤波(UKF)数学模型。Un-scented变换基于高斯分布理论,通过Sigma点能够获取精确到三阶矩的均值和协方差,提高了滤波精度。计算仅涉及标准的向量和矩阵操作,不需要计算非线性函数的Jacobian或者Hessians矩阵,提高了滤波速度。通过设计的运动实验进行仿真对比,实验结果表明,对于非线性目标跟踪系统,UKF算法具有更高的滤波精度和稳定性。

To aim at raising the speed and precision,Unscented transform is drawn into Kalman filtering,and the model of UKF is published. Because Unscented transform is based on the theory of Gaussian distribution,so the typical value and covariance has third-order precise through Sigma spots in this method,the precision of filtering is raised. It only involves the operations of standard vector and matrix,has no use for the calculation of non-liner matrix of Jacobian and Hessians,so the speed of filtering is increased. Through movement emulation,it shows that for nonlinearity target tracking system,UKF gives better accuracy and stability.