We study sensor/agent data collection and collaboration policies for parameter estimation, accounting for resource constraints and correlation between observations collected by distinct sensors/agents. Specifically, we consider a group of sensors/agents each samples from different variables of a multivariate Gaussian distribution and has different estimation objectives, and we formulate a sensor/agent's data collection and collaboration policy design problem as a Fisher information maximization (or Cramer-Rao bound minimization) problem. When the knowledge of correlation between variables is available, we analytically identify two particular scenarios: (1) where the knowledge of the correlation between samples cannot be leveraged for collaborative estimation purposes and (2) where the optimal data collection policy involves investing scarce resources to collaboratively sample and transfer information that is not of immediate interest and whose statistics are already known, with the sole goal of increasing the confidence on the estimate of the parameter of interest. When the knowledge of certain correlation is unavailable but collaboration may still be worthwhile, we propose novel ways to apply multi-armed bandit algorithms to learn the optimal data collection and collaboration policy in our distributed parameter estimation problem and demonstrate that the proposed algorithms, DOUBLE-F, DOUBLE-Z, UCB-F, UCB-Z, are effective through simulations.

翻译：我们研究了传感器/智能体在参数估计中的数据采集与协作策略，考虑了资源约束以及不同传感器/智能体观测值之间的相关性。具体而言，我们考虑一组传感器/智能体，每个传感器/智能体从多元高斯分布的不同变量中采样，并具有不同的估计目标。我们将传感器/智能体的数据采集与协作策略设计问题建模为Fisher信息最大化（或Cramér-Rao界最小化）问题。在变量间相关性已知的情况下，我们分析性地识别出两种特定场景：（1）样本间的相关性知识无法用于协作估计目的；（2）最优数据采集策略涉及将稀缺资源投入到协作采样和传输非直接感兴趣且统计特性已知的信息，其唯一目的是提高对感兴趣参数估计的置信度。当某些相关性知识未知但协作仍可能有益时，我们提出应用多臂老虎机算法的新方法，以在分布式参数估计问题中学习最优数据采集与协作策略，并通过仿真证明所提出的算法（DOUBLE-F、DOUBLE-Z、UCB-F、UCB-Z）是有效的。