高等学校岗位绩效工资实施策略研究

在非线性条件下，扩展Kalman滤波(EKF)的应用最为广泛。但是，由于它采用了Taylor展开的线性变换来近似非线性模型，因而存在计算量大、实时性差、估计精度低等缺点。粒子滤波(PF)用一些带有权值的随机样本(粒子)来表示所需要的后验概率密度，并通过这些粒子的加权来估计目标运动的状态，从而得到基于物理模型的近似最优数值解，具有精度高、收敛速度快等特点。通过仿真实验将PF与EKF的性能进行了对比，并且研究了噪声协方差与粒子数对PF的影响。PF与EKF的对比实验结果表明，在强非线性条件下，PF比EKF跟踪精

Research on Implementation Strategy for Job Performance based Payment in Higher Education

The Extended Kalman Filter (EKF) is the most popular approach to recursive nonlinear estimation. Because it is a linearization technique based on a first order Taylor series expansion of the nonlinear system and measurement functions about the current estimate of the state, it often provides an insufficiently accurate representation in many cases. The particle filtering method (PF) has become an important alternative to the EKF. It represents the desired distributions by discrete random measures, which are composed of weighted particles. It has a high accuracy and a rapid convergence. In this paper, the comparison experiment between the PF and EKF has been carried out, and a study about the influence of the particle filter by the noise covariance and the particle number are presented. The comparison experiment shows that the target tracking accuracy of PF is higher than that of EKF under the condition of strong non linear system.