In this article, the Tobit Kalman filtering problem is investigated for a class of discrete time‐varying fractional‐order systems in the presence of measurement censoring and stochastic nonlinearities under the Round‐Robin protocol (RRP). The fractional‐order dynamic model is described by the Grunwald–Letnikov difference equation, and the statistical means are utilized to characterize the stochastic nonlinearities that include state‐dependent stochastic disturbances as a special case. The RRP is employed to decide the transmission sequence of sensors so as to alleviate undesirable data collisions. Under the RRP scheduling, only one sensor is permitted to transmit its measurement over the network at each time instant. In light of the renowned orthogonality projection principle, a protocol‐based fractional Tobit Kalman filter is devised with the fractional dynamics and stochastic nonlinearities elaborately addressed. In the pursuit of the filter design, a couple of new terms appear which are in relation to the RRP, fractional dynamics and stochastic nonlinearities arise, and these terms are adequately handled recursively or off‐line. Simulation results are provided to demonstrate the usefulness of the proposed method.

中文翻译：

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Round-Robin协议下具有随机非线性分数阶系统的Tobit Kalman滤波

在本文中，研究了在Round-Robin协议（RRP）下存在测量检查和随机非线性的情况下，一类离散时变分数阶系统的Tobit Kalman滤波问题。分数阶动力学模型由Grunwald-Letnikov差分方程描述，并采用统计方法来表征随机非线性，其中包括与状态有关的随机扰动，这是特例。RRP用于确定传感器的传输顺序，以减轻不希望的数据冲突。在RRP调度下，每个时刻仅允许一个传感器通过网络传输其测量值。根据著名的正交投影原理，设计了基于协议的分数Tobit Kalman滤波器，并详细解决了分数动力学和随机非线性问题。在追求滤波器设计的过程中，出现了一些与RRP相关的新术语，分数动态和随机非线性出现了，这些术语可以递归或离线地适当处理。仿真结果证明了该方法的有效性。