Cooperative adaptive sampling of random fields with partially known covariance
Cooperative adaptive sampling of random fields with partially known covariance
复制标题
具有部分已知协方差的随机场的协作自适应采样
DOI：
10.1002/rnc.1710
复制
发表时间：
2012
影响因子：
3.9
通讯作者：
J. Cortés
中科院分区：
文献类型：
--
作者：
Rishi Graham;J. Cortés
本文考虑自主机器人传感器网络对物理过程进行测量以达到预测目的。将物理过程模拟为时空随机场。网络的目标是在最大限度地增加数据信息量的地点进行抽样。基于信息的优化和分布式控制的结合带来了困难的技术挑战，因为信息的标准措施本质上不是分布式的。此外，缺乏关于该领域统计结构的先验知识可能会使这一问题变得任意困难。假设场的均值是已知函数的未知线性组合，其协方差结构由已知到未知参数的函数决定，我们提供了一种新的分布式方法，用于由静态设备和移动设备组成的网络进行序贯优化设计。我们对所提出的算法的正确性进行了表征，并详细分析了其实现所需的时间、通信和空间复杂性。版权所有©2011 John Wiley&Sons，Ltd.
This paper considers autonomous robotic sensor networks taking measurements of a physical process for predictive purposes. The physical process is modeled as a spatiotemporal random field. The network objective is to take samples at locations that maximize the information content of the data. The combination of information‐based optimization and distributed control presents difficult technical challenges as standard measures of information are not distributed in nature. Moreover, the lack of prior knowledge on the statistical structure of the field can make the problem arbitrarily difficult. Assuming the mean of the field is an unknown linear combination of known functions and its covariance structure is determined by a function known up to an unknown parameter, we provide a novel distributed method for performing sequential optimal design by a network comprised static and mobile devices. We characterize the correctness of the proposed algorithm and examine in detail the time, communication, and space complexities required for its implementation. Copyright © 2011 John Wiley & Sons, Ltd.