Nonlinear State Estimation With Multisensor Stochastic Scheduling
研究了多传感器随机调度下非线性系统状态估计问题，用无迹变换处理非高斯性，提出随机事件触发机制降低网络传输负担，并给出保证误差协方差和估计误差稳定性的充分条件。
In this article, the problem of a nonlinear system states estimation with multisensor stochastic scheduling is investigated. In order to solve the non-Gaussian property induced by the nonlinear transformation, the unscented transformation (UT) technique is applied. Since the sensor networks channel is limited, the stochastic event-triggered mechanisms (SETMs) are proposed to reduce the network transmission burden. Under the SETMs, the modified unscented Kalman filter is proposed. Additionally, the sufficient conditions are given to guarantee the stabilities of the error covariance and the estimation error. Finally, extensive examples are carried out. Performances evaluation and comparison with existing methods are given to demonstrate the superiority of the proposed methods.