基于信息融合理论的卡尔曼滤波及仿真

Kalman Filtering Based on Information Fusion Theory and Simulation

对于存在控制及测量噪声,状态变量受到污染的系统,控制过程要求排除噪声干扰。本文采用信息融合理论,对传统卡尔曼滤波算法进行改进,得到新的滤波算法。有效的排除噪声干扰并通过仿真验证。

When the systems have disturbed by the control and the survey noise, and the state variable was polluted, the controlled process requests to exclude the noise disturbance. In this paper, based on the information fusion theory, the traditional Kalman Filtering algorithm improved to a new filtering algorithm. Effective exclude noise and verified by simulation.