Zhang Hao, Zhou Xue, Wang Zhuping, Yan Huaicheng
IEEE Trans Cybern. 2020 Oct;50(10):4346-4357. doi: 10.1109/TCYB.2019.2901515. Epub 2019 Apr 17.
In this paper, the distributed remote state estimation problem for conditional dynamic linear systems in mobile sensor networks with an event-triggered mechanism is investigated. The distributed mixture Kalman filtering method is proposed to track the state of the maneuvering target, which uses particle filtering to estimate the nonlinear variables and apply Kalman filtering to estimate the linear variables. An event-based distributed filtering scheme is designed, which is an energy-efficient way to transmit data between sensors and estimators. In addition, by using the mutual information theory, an optimal control problem is formed to control the position of sensors so that the target tracking process can be achieved quickly. Finally, a simulation example about the maneuvering target tracking is provided to corroborate the effectiveness of the filtering method and the control performance for sensors.
本文研究了具有事件触发机制的移动传感器网络中条件动态线性系统的分布式远程状态估计问题。提出了分布式混合卡尔曼滤波方法来跟踪机动目标的状态，该方法使用粒子滤波估计非线性变量，并应用卡尔曼滤波估计线性变量。设计了一种基于事件的分布式滤波方案，这是一种在传感器和估计器之间传输数据的节能方式。此外，利用互信息理论，形成了一个最优控制问题来控制传感器的位置，以便快速实现目标跟踪过程。最后，给出了一个关于机动目标跟踪的仿真例子，以证实滤波方法的有效性和传感器的控制性能。
