This novel is concerned with the event-triggering robust fusion estimation problem for multi-rate systems (MRSs) subject to stochastic nonlinearities (SNs) and censored observations (COs). The considered multi-rate system includes several sensor nodes, and each sensor is with different sampling rate. To reflect the dead-zone-like censoring phenomenon, a Tobit-1 regression model with prescribed left-censoring threshold is introduced, and the stochastic nonlinearities characterized by statistical means are considered in the MRSs. In order to save the limited resource, the event-triggering mechanism (ETM) has been introduced to determine whether the specified sensor node should transmit the information to the corresponding local filter. For the addressed MRSs, we aim to design a local Tobit Kalman filtering (TKF) algorithm for each sensor node firstly in the sense of the upper bound on each local filtering error covariance being minimal. Then, such a minimized upper bound is derived by designing the filter gain properly at each iteration. In the sequel, the fusion centre manipulates the local estimates by the CI scheme. Moreover, we discuss the issue of consistency for the proposed multi-rate fusion estimation (MRFE) approach. At last, experimental simulation are exploited to demonstrate the validation of the designed MRFE algorithm.

这本小说涉及的事件触发鲁棒融合估计问题的多速率系统（MRS）受随机非线性（SNs）和审查的意见（COs）。所考虑的多速率系统包括多个传感器节点，并且每个传感器具有不同的采样率。为了反映类似死区的检查现象，引入了具有规定左检查阈值的Tobit-1回归模型，并在MRS中考虑了以统计手段表征的随机非线性。为了节省有限的资源，已引入事件触发机制（ETM），以确定指定的传感器节点是否应将信息传输到相应的本地筛选器。对于已解决的MRS，我们旨在首先在每个局部滤波误差协方差的上限最小的意义上为每个传感器节点设计一种局部Tobit卡尔曼滤波（TKF）算法。然后，通过在每次迭代中适当地设计滤波器增益来推导出这样的最小化的上限。在续集中，融合中心通过CI方案操纵局部估计。此外，我们讨论了所提出的多速率融合估计（MRFE）方法的一致性问题。最后，通过实验仿真来证明所设计的MRFE算法的有效性。此外，我们讨论了所提出的多速率融合估计（MRFE）方法的一致性问题。最后，通过实验仿真来证明所设计的MRFE算法的有效性。此外，我们讨论了所提出的多速率融合估计（MRFE）方法的一致性问题。最后，通过实验仿真来证明所设计的MRFE算法的有效性。