论文标题
分布式过滤具有信息审查的价值
Distributed Filtering with Value of Information Censoring
论文作者
论文摘要
这项工作提出了有效使用可用通信资源的分布式估计算法。该方法基于使用对数意见池操作员在网络上分布的贝叶斯过滤。通信效率是通过仅拥有具有高信息价值（VOI）共享其估计的代理来实现的，并且该算法在通信资源和估计错误之间提供了可调的权衡。在线性高斯模型下，算法采用了审查的分布式信息过滤器的形式，该滤波器保证了代理估计的一致性。重要的是，一致的估计表明，在VOI审查方法提供的通信使用情况下大量降低中起着至关重要的作用。我们通过动态网络拓扑中的复杂模拟以及对实际临时无线通信网络的实验验证来验证所提出的方法的性能。结果表明，使用建议的方法大幅度降低分布式估计任务的通信成本。
This work presents a distributed estimation algorithm that efficiently uses the available communication resources. The approach is based on Bayesian filtering that is distributed across a network by using the logarithmic opinion pool operator. Communication efficiency is achieved by having only agents with high Value of Information (VoI) share their estimates, and the algorithm provides a tunable trade-off between communication resources and estimation error. Under linear-Gaussian models the algorithm takes the form of a censored distributed Information filter, which guarantees the consistency of agent estimates. Importantly, consistent estimates are shown to play a crucial role in enabling the large reductions in communication usage provided by the VoI censoring approach. We verify the performance of the proposed method via complex simulations in a dynamic network topology and by experimental validation over a real ad-hoc wireless communication network. The results show the validity of using the proposed method to drastically reduce the communication costs of distributed estimation tasks.
