粒子滤波广泛应用于对精度和稳定性要求较高的目标跟踪，但其计算量大，并且计算复杂度随着状态量和粒子数目增长迅速增加。将目标跟踪转化为由粗到精的搜索过程，提出了一种基于精确运动模型的改进分层卡尔曼粒子滤波算法。该方法利用加速度的运动模型在真实目标位置的周围估计目标的散布范围，并在该范围内随机生成粒子，寻找精确的目标位置。文中引入加加速度模型主要是由于现有方法的状态量阶数不足，导致模型精确度较低，无法应对大机动目标的跟踪。因此，引入了高阶状态变量加加速度，并将其用于改进分层卡尔曼粒子滤波的运动模型。利用分层卡尔曼粒子滤波、粒子滤波以及提出的方法进行了跟踪试验，结果表明，基于精确运动模型的改进分层卡尔曼粒子滤波模型的跟踪方法能够提高线性运动的预测精度，实现复杂环境下精确稳定的跟踪。

Particle filter is widely used for visual tracking with superior performance in terms of accuracy and robustness, but it suffers from the heavy computational load, and the calculation complexity increases quickly with the state dimension and the number of particles. In this paper, the tracking problem was considered as a coarse-to-fine process to find the optimal state, thus, a hierarchical Kalman-particle filter (HKPF) with precise motion model, called improved hierarchical Kalman-particle filter (IHKPF), was proposed, in which Kalman filter with Jerk model was used to predict a local region around the estimation of global linear motion, and then particles were generated in the local region. The reason for introducing Jerk model was that the inadequate tracking performance of current models with the higher order derivating in the case of very highly maneuvering targets were not tracked therefore, Jerk was added. The high order state variable Jerk was applied in motion model of IHKPF. The HKPF, PF and proposed in the paper were used to compelete track experiment. The experimental results among the proposed algorithm HKPF and PF indicate that Jerk model provides higher accuracy prediction, resulting in well- behaved tracking in complex environment.