基于联合卡尔曼滤波的多传感器信息融合算法及其应用

针对常见卡尔曼滤波器在处理多传感器组合系统的数据时，存在计算量大和故障数据相互污染的问题，提出了一种应用联合卡尔曼滤波技术进行多传感器信息融合，以求得参数最优估计的方法。文中首先对联合卡尔曼滤波的基本原理和4种主要结构方式进行了论述和分析，然后给出了融合算法的实现，最后以多传感器组合导航系统为例，对其进行计算机仿真。结果表明，该方法可有效提高计算的精度和可靠性，具有较好的容错性和环境适应性，有效高的工程实用价值。

Multi-sensor information fusion algorithm based on federal Kalman filter and its application

In view of the heavy calculation and fault-data spread in multi-sensor integrated sys- tem when using general Kalman filter, a new method of optimum parameter estimate based on information fusion by means of federal Kalman filter is presented. Firstly, the basic principle and four frames of the federal Kalman filter is introduced, then the algorithm is given. Simulation results show this method is very useful in project because it can improve accuracy and reliability, at the same time it has high fault-tolerant and adaptive ability.