论文标题
部分可观测时空混沌系统的无模型预测
Robust Distributed Kalman filtering with Event-Triggered Communication
论文作者
论文摘要
在情况下，我们考虑了数据传输的限制，并且存在模型不确定性，我们考虑了传感器网络的分布式Kalman过滤问题。更确切地说，我们提出了两种分布式过滤策略，并通过事件触发的通信，其中根据最不利的模型计算状态估计器。后者属于关于名义模型的球。我们还表明，这两种方法都是稳定的，从某种意义上说，状态估计误差的平均值在所有节点中都有界限。
We consider the problem of distributed Kalman filtering for sensor networks in the case there are constraints in data transmission and there is model uncertainty. More precisely, we propose two distributed filtering strategies with event-triggered communication where the state estimators are computed according to the least favorable model. The latter belongs to a ball about the nominal model. We also show that both the methods are stable in the sense that the mean-square of the state estimation error is bounded in all the nodes.
