Optimal Field Sensing Strategies for Time-Critical Estimation and Prediction of Dynamic Environments

该项目旨在开发一个系统的框架和可扩展的计算技术，用于传感器的放置和时间关键的传感器运动策略，用于动态演变，空间分布的场量。这些场描述环境变量，如羽流浓度、压力、风速或数据同化技术中常见的海洋盐度和温度。越来越普遍的移动的传感平台提供了一个机会，以改善传统的估计和预测技术的数据同化。 该项目的一个主要目标是发现如何放置或覆盖有限数量的传感器，以时间关键的方式优化估计和预测保真度。该项目的成果将使新的设计技术，最终可以在自然灾害预测和响应管理援助。从森林火灾前沿到洪水和其他恶劣天气事件，数据同化技术目前是预测不可或缺的。然而，它们通常受到仅使用暂时可用的环境测量的限制。这项研究将产生一个系统的方法，积极主动的，最佳的调度和轨迹规划的移动的传感器，其测量，然后可以减少预测的不确定性的关键区域和数量。这将大大有助于自然灾害应急准备和管理。该项目的成功不仅将促进估计和预测技术的基础科学，而且有助于促进国家的发展。的灾害预测和应对能力。采用基于模型的估计和预测方法，其中基础物理定律的使用使得能够从稀疏和有限的测量对时空变化的物理场进行高分辨率估计和预测。该项目的主旨是开发一个新的动态探索框架，其中传感器的运动和/或位置是使用最佳和反馈控制技术设计的，目标是最大限度地提高信息增益指标。虽然运动学可以很容易地在个人设置开发，有必要为估计器优化的目的，特别是在动态环境中的运动控制设计的系统理论。因此，在一个未知的环境中的传感器运动问题被重新制定为一个最优控制问题的目标是最大化的信息奖励度量，或最小化的误差协方差。需要开发新的技术来解决这些非传统的最优和反馈控制问题。大规模的计算问题，如误差协方差的低秩近似将被探索和利用。该项目将解决由于传感模式之间的差异而产生的具体研究问题，例如逐点与断层扫描或聚集体sensing.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

This project aims to develop a systematic framework and scalable computational techniques for sensor placement and time-critical sensor motion strategies for dynamically evolving, spatially-distributed field quantities. Such fields describe environmental variables such as plume concentrations, pressures, wind velocity, or ocean salinity and temperature as is common in techniques of data assimilation. The increasing ubiquity of mobile sensing platforms presents an opportunity to improve upon traditional estimation and prediction techniques in data assimilation. A major goal of this project is to discover how a limited number of sensors should be placed or maneuvered to optimize estimation and prediction fidelity in a time-critical manner. The results of this project will enable new design techniques that can ultimately aid in natural disaster prediction and response management. From forest fire fronts, to floods and other severe weather events, data assimilation techniques are currently indispensable for prediction. They are however typically constrained by the use of only the momentarily available environmental measurements. This research would produce a systematic methodology for proactive, optimal dispatching and trajectory planning of mobile sensors whose measurements can then reduce prediction uncertainty for critical regions and quantities. This would be a significant aid to natural disaster response preparation and management. The success of this project will not only promote the fundamental science in estimation and predictive technologies but also help advance the nation?s disaster prediction and response capability. A model-based estimation and prediction approach is adopted, where the use of underlying physical laws enable high resolution estimation and prediction of spatio-temporally varying physical fields from sparse and limited measurements. The main thrust of the project is the development of a new framework of dynamic exploration, in which sensors' motion and/or location is designed using optimal and feedback control techniques with the objective of maximizing information gain metrics. While heuristics can be easily developed in individual settings, there is a need for a systematic theory of motion control design for the purpose of estimator optimization, especially in dynamic environments. Thus the sensor motion problem in an unknown environment is reformulated as an optimal control problem with the objective being the maximization of information reward metrics, or minimization of error covariances. New techniques will need to be developed to address these non-traditional optimal and feedback control problems. Large-scale computational issues such as low-rank approximations of error covariances will be explored and utilized. The project will address specific research questions that arise due to differences between sensing modalities such as point-wise versus tomographic or aggregates sensing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.