In this paper, a distributed extended Kalman filter (EKF) is developed for a class of nonlinear systems, whose outputs are measured by multiple sensors which send data using an event triggered mechanism through a communication network subject to loss and latency. Random transmission delay and multiple dropouts are modelled by a Bernoulli random sequence. The filter gains are determined in each sensor node such that an upper bound on the cross covariance of the estimation error is minimized; so, less computational burden is required, even in the networks with the large number of nodes. To be specific, the scalability is the main feature of the proposed scheme. The boundedness of the filtering error is proved under some conditions. Finally, comparative simulation results are presented to illustrate the effectiveness and the applicability of the suggested filter.

在本文中，针对一类非线性系统开发了分布式扩展卡尔曼滤波器（EKF），其输出由多个传感器测量，这些传感器使用事件触发机制通过通信网络发送数据，这会受到损失和等待时间的影响。随机传输延迟和多个丢失通过伯努利随机序列建模。确定每个传感器节点中的滤波器增益，以使估计误差的交叉协方差的上限最小。因此，即使在具有大量节点的网络中，也需要较少的计算负担。具体而言，可伸缩性是所提出方案的主要特征。在某些条件下证明了滤波误差的有界性。最后，