.png){kind=icon}
返回
A complete greedy algorithm for infinite-horizon sensor scheduling
.png){kind=icon}
PRE
AI
DOI：10.1016/j.automatica.2017.04.018.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we study the problem of scheduling sensors to estimate the state of a linear dynamical system. The estimator is a Kalman filter and our objective is to optimize the a posteriori error covariance over an infinite time horizon. We focus on the case where a fixed number of sensors are selected at each time step, and we characterize the exact conditions for the existence of a schedule with uniformly bounded estimation error covariance, Using this result, we construct a scheduling algorithm that guarantees that the error covariance will be bounded if the existence conditions are satisfied. We call such an algorithm complete. Finally, we provide simulations to compare the performance of the algorithm against other known techniques. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Sensor networks
Kalman filters
Sensor scheduling
Multi-sensor estimation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.9
论文数:
1.2W
被引数:
5.2W
机构
引用论文
Submodularity and greedy algorithms in sensor scheduling for linear dynamical systems线性动态系统的传感器调度中的亚模块化和贪婪算法.png)
AUTOMATICA
AUTOMATICAIF5.9
PREAI
分享收藏
查看更多内容