论文标题
通过基于事件的策略减少通信的分布式估计框架
A Framework for Distributed Estimation with Reduced Communication via Event-Based Strategies
论文作者
论文摘要
本文考虑了传感器网络中分布式估计的问题，其中部署了多个传感器以推断线性时间不变（LTI）高斯系统的状态。通过提出对卡尔曼过滤器的无损分解，开发了一个基于事件的分布式估计的框架，在该框架中，每个传感器节点仅使用其自身测量值以及基于事件的同步算法运行局部过滤器，以融合相邻信息。提出的框架的一种新颖性是，它将局部过滤器与同步过程解剖。通过这样做，我们证明可以在我们的框架中应用一般的触发策略，该策略在网络连接性和集体系统可观察性的最低要求下产生稳定的分布式估计器。与现有作品相比，所提出的算法对于每个变速箱的数据尺寸都较低。此外，可以将开发的结果推广以实现任何Luenberger观察者的分布式实现。通过求解半准编程（SDP），我们进一步提出了低级估计器设计，以获得Luenberger观察者的最佳增益，以便在消息复杂性的约束下实现了分布式估计。最终提供了数值示例以证明所提出的方法。
This paper considers the problem of distributed estimation in a sensor network, where multiple sensors are deployed to infer the state of a linear time-invariant (LTI) Gaussian system. By proposing a lossless decomposition of Kalman filter, a framework of event-based distributed estimation is developed, where each sensor node runs a local filter using solely its own measurement, alongside with an event-based synchronization algorithm to fuse the neighboring information. One novelty of the proposed framework is that it decouples the local filter from synchronization process. By doing so, we prove that a general class of triggering strategies can be applied in our framework, which yields stable distributed estimators under the minimal requirements of network connectivity and collective system observability. As compared with existing works, the proposed algorithm enjoys lower data size for each transmission. Moreover, the developed results can be generalized to achieve a distributed implementation of any Luenberger observer. By solving a semi-definite programming (SDP), we further present a low-rank estimator design to obtain the optimal gain of Luenberger observer such that the distributed estimation is realized under the constraint of message complexity. Numerical examples are finally provided to demonstrate the proposed methods.
