传感器网络的序贯协方差交叉反馈融合状态滤波器

Sequential CI feedback fusion state filter for sensor networks

为了减轻传感器网络融合中心的计算负担和实现传感器信息的充分利用,提出了一种带反馈结构的序贯协方差交叉融合Kalman滤波器。该反馈结构将传感器网络融合中心的状态融合预报值及其融合误差方差信息反馈给局部传感器作为先验信息,进行局部滤波,将得到的新局部状态滤波值和局部滤波误差方差阵信息传递到融合中心,根据到达次序进行序贯协方差交叉融合。搭建了带反馈结构的序贯融合算法框架。仿真实验表明,该算法具有良好的估计性能,且明显减少了融合中心的计算负担,与集中式融合滤波器相比,减小了融合中心的计算压力与负担,提高网络的容错性,与不带反馈的序贯协方差交叉融合滤波器相比,具有更高的估计精度。

In order to reduce the computational burden of the sensor networked fusion center and fully utilize the sensor information,a sequential CI fusion Kalman filter with feedback structure is presented.This feedback structure sends back the state fusion prediction and the fusion error variance information of the fusion center to the local sensors as prior information,the filters work separately to calculate the new local state filter and its filtering error variance at the local sensor,which will be sent to the fusion center,and then the sequential CI fusion will be made according to the arriving orders of local estimators.A sequential fusion algorithm framework with feedback structure is established.Simulation experiments shows that this algorithm has good estimation performance and significantly reduces the computational burden of the fusion center.Compared with the centralized fusion filters,this algorithm reduces the computational pressure and burden of the fusion center,and improves the fault tolerance of the network.It has higher estimation accuracy than the sequential CI fusion filters without feedback.