arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1609.07534eess.SYcs.SYmath.OC
收藏

预测与自触发事件驱动状态估计

Predictive and Self Triggering for Event-based State Estimation

Max Planck Institute for Intelligent Systems(马克斯·普朗克智能系统研究所)

Sebastian Trimpe

AI总结:

本文提出预测与自触发协议,通过统一的贝叶斯决策框架,实现通信资源的优化分配,提升事件驱动状态估计的效率与质量。

摘要:

事件驱动的状态估计可以实现与传统时间触发方法相当的估计质量,但样本数显著减少。在网络估计问题中,这种采样减少并不一定意味着通信资源的更有效利用。因为典型的事件驱动方法即时决定是否需要通信,导致空闲槽位无法立即重新分配,因此被浪费。本文提出新颖的预测和自触发协议,使通信系统有时间适应和重新分配释放的资源。从统一的贝叶斯决策框架中,开发了两种方案:自触发器在当前触发时刻预测下一个触发时刻;预测触发器在每个时间步长上指示在给定预测时间范围内是否需要通信。所提出的触发器在数值模拟中比较了在牺牲估计质量以换取通信减少方面的有效性。

英文摘要:

Event-based state estimation can achieve estimation quality comparable to traditional time-triggered methods, but with a significantly lower number of samples. In networked estimation problems, this reduction in sampling instants does, however, not necessarily translate into better usage of the shared communication resource. Because typical event-based approaches decide instantaneously whether communication is needed or not, free slots cannot be reallocated immediately, and hence remain unused. In this paper, novel predictive and self triggering protocols are proposed, which give the communication system time to adapt and reallocate freed resources. From a unified Bayesian decision framework, two schemes are developed: self-triggers that predict, at the current triggering instant, the next one; and predictive triggers that indicate, at every time step, whether communication will be needed at a given prediction horizon. The effectiveness of the proposed triggers in trading off estimation quality for communication reduction is compared in numerical simulations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑