针对波动性较大目标跟踪，传统Kalman滤波算法鲁棒性和实时性不足，提出一种基于多尺度特征提取的Kalman跟踪算法.前帧目标区域特征点匹配出后续帧目标区域特征点，并以后者特征点为中心，建立搜索区域，避免了遍历整幅后续帧图像，快速地为Kalman滤波方程状态后验值提供了稳定的观测信号和观测残差.实验证明，这种作为约束条件引入传统的Kalman滤波方程的多尺度特征提取技术，克服了传统Kalman滤波时间较长，易发散的缺陷，从而使其有着良好的收敛性.

To overcome the deficiency of the robustness and real-time performance of traditional Kalman filter used for changeable target tracking, a new Kalman algorithm based on multi-scale feature extraction was proposed. After the feature points of a frame matched ones of the follow-up frame in the target area of image, the latter feature points centroid was took as the center from which the searching area was located so as to avoid traversing the whole image. So the stable signals and residuals of observations were provided to the Kalman filter equations to calculate accurately the posteriori state value. Experiment show that the multi-scale feature extraction technology introduced into the traditional Kalman filter equation as constrained conditions reduced filtering time and restrained divergence. Thus improved filter has good convergence.