无迹卡尔曼滤波算法在目标跟踪中的研究

无迹卡尔曼滤波(UKF)是重要的非线性滤波方法。无迹卡尔曼滤波方法是通过一组代表着均值和方差分布的采样点来对非线性系统进行非线性计算,在不对非线性方程线性近似的条件下,达到线性卡尔曼滤波器的滤波性能。文中在机动目标选定运动模型和滤波算法的基础上,对机动目标的运动作了仿真实验。从仿真分析中可以看出,无迹卡尔曼滤波在跟踪方面有很高的精度,与传统的扩展卡尔曼滤波算法相比较,无迹卡尔曼滤波算法有较小的跟踪误差。

Research of UKF in the target tracking

Unscented Kalman filtering is an important nonlinear filtering method.However,the application of the KF to nonlinear systems can be quite difficult.Using the principle that a set of discretely sampled points can be used to parameterize mean and covariance,the unscented Kalman filtering yields performance equivalent to the Kalman Filtering for linear systems yet generalizes elegantly to nonlinear systems without the linearization steps required by the extended Kalman filtering.This paper according to the model and the filter algorithm,the simulation experiments about the movement of the maneuvering target is done.To the conclude from the analyses of the simulation,the unscented Kalman filtering has high accuracy in tracking.With the comparison to the extended Kalman filtering,the unscented Kalman filtering has the less error of the tracking.