基于卡尔曼滤波对现有采样数据进行滤波，有效降低观测值的误差。卡尔曼滤波是一种时域方法，它把状态空间的概念引入随机估计理论，用状态方程、观测方程和噪声激励递推估计测量噪声，便于实现实时应用。(The existing sampled data is filtered based on Kalman filter, which can effectively reduce the error of the observed value. Kalman filtering is a time domain method. It introduces the concept of state space into the theory of stochastic estimation, and uses state equation, observation equation and noise excitation to estimate noise. It is easy for real-time applications.)