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

用于动态环境的时间关键估计和预测的最佳场传感策略

• 批准号: 1763064
• 负责人: Bassam Bamieh
• 金额: $ 35.5万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763064&HistoricalAwards=false
• 关键词: Optimal; Field; Sensing; Strategies; Time

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.

该项目旨在开发一个系统的框架和可扩展的计算技术，用于传感器布置和动态演变的、空间分布的场量的时间关键传感器运动策略。这些场描述了数据同化技术中常见的环境变量，如烟羽浓度、压力、风速或海洋盐度和温度。移动传感平台的日益普及为改进数据同化中的传统估计和预测技术提供了机会。这个项目的一个主要目标是发现应该如何放置或操纵有限数量的传感器，以在时间关键的方式下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能够有助于自然灾害的预测和应对管理。从森林火灾前锋到洪水和其他恶劣天气事件，数据同化技术目前对于预测是不可或缺的。然而，它们通常受到仅使用暂时可用的环境测量的限制。这项研究将为移动传感器的主动、优化调度和轨迹规划提供一种系统的方法，其测量结果可以减少关键区域和数量的预测不确定性。这将是对自然灾害应急准备和管理的重大帮助。该项目的成功，不仅将促进我国估计和预测技术的基础科学研究，而且将有助于提高国家的S灾害预测和应对能力。采用基于模型的估计和预测方法，其中使用基本的物理定律使得能够从稀疏和有限的测量中高分辨率地估计和预测时空变化的物理场。该项目的主要目的是开发一种新的动态探测框架，其中传感器的运动和/或位置是使用最优和反馈控制技术设计的，目标是最大化信息增益度量。虽然启发式算法可以很容易地在个人环境中开发，但为了估计器优化的目的，特别是在动态环境中，需要运动控制设计的系统理论。因此，传感器在未知环境中的运动问题被转化为一个最优控制问题，目标是最大化信息报酬度量，或最小化误差协方差。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，如误差协方差的低阶近似，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，例如逐点传感与断层扫描或聚合传感之间的差异。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

An Explicit Parametrization of Closed Loops for Spatially Distributed Controllers With Sparsity Constraints

• DOI: 10.1109/tac.2021.3111863
• 发表时间: 2020-12
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: E. Jensen;Bassam Bamieh

A Tutorial on Solution Properties of State Space Models of Dynamical Systems

动力系统状态空间模型解性质教程

• DOI: 10.48550/arxiv.2204.06104
• 发表时间: 2022
• 期刊: ArXivorg
• 作者: Bamieh, Bassam

Stochasticity in Feedback Loops: Great Expectations and Guaranteed Ruin

反馈循环中的随机性：远大的期望和注定的毁灭

• DOI: 10.1109/mcs.2020.3048453
• 发表时间: 2021
• 期刊: IEEE Control Systems
• 作者: Smith, Roy S.;Bamieh, Bassam
• 通讯作者: Bamieh, Bassam

Frequency Response Analysis of Parametric Resonance and Vibrational Stabilization

参数共振和振动稳定的频率响应分析

• DOI: 10.23919/acc45564.2020.9147210
• 发表时间: 2020
• 期刊: 2020 American Control Conference
• 作者: Chikmagalur, Karthik;Bamieh, Bassam
• 通讯作者: Bamieh, Bassam

An Input–Output Approach to Structured Stochastic Uncertainty

结构化随机不确定性的输入输出方法

• DOI: 10.1109/tac.2020.2970393
• 发表时间: 2020
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Bamieh, Bassam;Filo, Maurice
• 通讯作者: Filo, Maurice