分层卡尔曼粒子滤波成功应用于目标跟踪，但其只对目标位置进行了优化，忽略了其他仿射参数，导致跟踪中的粒子数目仍然很大。为了实现复杂环境下的快速目标跟踪，提出一种带有自调整策略的分层卡尔曼粒子滤波方法。该方法将目标划分为线性和非线性状态空间，并通过少量粒子的迭代过程在非线性状态空间逐步搜索最优状态。其详细过程如下：首先，利用卡尔曼滤波预测目标位置，结合目标运动信息计算潜在目标区域；然后在该区域内生成一组随机粒子，通过在线姿态估计对粒子状态进行调整，并将观测结果与目标模板进行比较，修正粒子摄动的方向以逼近目标。把该方法应用于大机动目标的视频序列中，并与现有的跟踪方法进行了对比。结果表明，所提方法能够以少量粒子实现准确、稳定的目标跟踪，大大降低了跟踪算法的运算量，提高了跟踪效果。

Hierarchical Kalman-particle filter(HKPF) is successfully applied to target tracking with adaption to motion changes. However, it only focuses on the optimization of the target position rather than other affine parameters, resulting in many particles needed to find the optimal state. To achieve fast tracking in complex environment, self-tuning strategy-based hierarchical Kalman-particle filter was proposed to solve the problem. The proposed algorithm marginalized out the linear states in the dynamics to reduce the state dimension, and then found the optimal nonlinear states in a chainlike way with a very small number of particles. The detail process of our algorithm was as follows: first, a local region was estimated by KF; second, self-tuning strategy was used to incrementally generate particles in this region, and an online-learned pose estimator(PE) was introduced to iteratively tune them along the optimal directions according to observations. The comparison among the proposed algorithm and the existing tracking algorithms with real video sequences was implemented, in which the target undergo rapid and erratic motion, or/and dramatic pose change. The results demonstrate that the proposed tracking algorithm can achieve great robustness and very high accuracy with only a very small number of particles.