在视频图像运动目标的状态估计与跟踪问题中，常用的扩展卡尔曼(EKF)算法简单、计算量小，但仅适用于弱非线性和弱高斯环境下。本文提出一种基于无迹卡尔曼滤波(UKF)与简化交互多模型(IMM)算法相结合的视频图像运动目标跟踪算法，有效地克服了EKF 算法在强非线性状态下或对小运动目标跟踪时精度低，容易发散的问题。仿真结果表明，该算法估计和跟踪非线性目标的性能明显优于基于EKF 算法，其跟踪精度可达到三阶(泰勒级数展开)精度。

For tracking and measuring maneuvering target for video image, Extended Kalman filter (EKF) based on local linearization of KF is easy to be realized , but only has performance in Gaussian and mild nonlinear environment. An algorithm based on Unscented Kalman Filter (UKF) and Interaction Multiple Model (IMM) is proposed for maneuvering target tracking in complex nonlinearity environment or tracking small object in video image. The simulation results show that the tracking performance of UKF is much better than EKF in complex nonlinearity environment, and the third order tracking precision can be achieved.