事件触发容积卡尔曼滤波及其在火控探测网络中的应用

为减小传感器网络中各探测单元和融合中心间的数据传输量，设计了基于双重判断准则的事件触发机制，以控制各探测单元到融合中心的数据传输。为得到比已有事件触发融合估计算法更高的估计精度，提出了一种基于所提事件触发机制和平方根容积卡尔曼滤波的非线性融合估计方法。仿真和试验结果表明：所提方法与基于周期传输机制的容积卡尔曼滤波相比，可减小数据传输量；与已有事件触发融合估计方法相比，可在数据传输量基本一致情况下获得更高的估计精度。

Event-triggered Cubature Kalman Filtering Algorithm and Its Application in the Detection Network for Fire Control

An event-triggered mechanism based on dual-criterion is designed to reduce the amount of data transfer between each detection unit and the data fusion center in a sensor network.The proposed eventtriggered mechanism has been utilized to schedule the data transfer between each detection unit and the data fusion center.And a nonlinear fusion estimator based on the aforementioned event-triggered mechanism and the square-root cubature Kalman filter is proposed to obtain better estimation performance than those of existing event-triggered fusion estimation algorithms.The simulated results and experimental results show that the proposed event-triggered fusion estimator can reduce the data transfer amount compared with the fusion estimator based on periodic transmission mechanism and offer better estimation performance than those of existing event-triggered fusion estimators.