一种带随机偏差的分布式信息融合滤波算法

在通信、计算机、信号处理、自动控制中，对于带有未知的干扰和偏差的随机系统的状态估计已经广泛出现。在现实环境中，不同的传感器可能受到不同的干扰影响。研究随机系统的状态估计问题在实际应用中具有重要的意义。对带有随机偏差的线性随机系统，将系统转换为多模型结构的特殊情况。利用最小方差的最优加权融合估计算法，获得了分布式信息融合滤波算法。通过仿真可以看出，分布式信息融合算法要比局部估计算法具有更高的精度，算法具有分布式结构，这使其具有更好的鲁棒性和可靠性。

A Distributed Information Fusion Filtering Algorithm with Random Deviation

The state estimation of stochastic system with unknown disturbance and deviation is widespread in communications,computer,signal processing,automatic control.In the real environment,different sen-sors can be affected by different interference .The research of stochastic system state estimation problem is of great importance in practical application .For linear stochastic systems with random deviation ,the sys-tem is transformed for model structure .Using the optimal weighted fusion estimation algorithm of minimum variance ,distributed information fusion filtering algorithm are obtained .We can see through the simulation that distributed information fusion algorithm has higher accuracy than the local estimation algorithm .The algorithm has the distributed structure ,making it have better robustness and reliability .