Modeling, Identification, and Estimation of Distributed Parameter Systems Using Mobile Sensor Networks

该项目将使用移动传感器网络（msn）制定通用数学框架，该框架将允许对复杂现实世界系统（如天气预报、野火控制、灾难恢复、爆炸物检测等）的行为信息进行有效和准确的预测。这种系统被称为分布参数系统（DPS），对这种系统进行建模并实时准确预测其行为是非常复杂和具有计算挑战性的。目前最先进的技术使用静态传感器网络来帮助从复杂的数学模型中获得解决方案，这些模型往往不准确，不能提供及时的信息。微信号网络的移动性和适应性使其成为克服这些挑战的理想选择。该项目将开发一个统一的框架，用于建模、识别、估计和预测这种分布式参数系统的行为。该项目还将通过为研究生提供跨学科和具有挑战性的研究经验，通过为本科生提供通过算法开发和试验台实验早期参与研究活动的机会，并通过通过大学的工程大使（EA）计划激励K-12学生的实践经验，从而开发一个强大的综合研究，教育和推广计划。该项目的成果有望推进分布式参数系统的建模、识别和状态估计技术，为DPS和移动传感器网络之间的联系提供全面的科学理解，并为设计移动传感器网络的协同控制和分布式感知策略提供通用的工程原理。具体而言，该工作将：开发使用移动传感器网络的DPS在线参数识别和状态估计的新方法，与为静态传感器网络开发的现有方法相比，减少了计算和通信成本；确定信息丰富、节能的移动传感器网络在DPS中运动的最优轨迹，同时进行系统识别和状态预测；设计了具有可控平流扩散场的多机器人试验台，对策略进行了验证，并进行了现场实验，在现实的不确定性和变化条件下对策略进行了测试。该项目还为研究生、本科生以及K-12学生提供了良好的教育机会。

This project will formulate a general purpose mathematical framework using mobile sensor networks (MSNs) that will allow an efficient and accurate prediction of information about behavior of complex real world systems such as weather forecasting, wildfire control, disaster recovery, explosive materials detection, etc. Such systems are known to be distributed parameter systems (DPS) and modeling of such systems and accurately predicting their behavior in real-time is highly complex and computationally challenging. The current state-of-the-art techniques use static sensor network to help obtain solutions from complex mathematical models which are often inaccurate and cannot provide timely information. The mobility and adaptability of MSNs make them great candidates for overcoming these challenges. This project will develop a unified framework for modeling, identifying, estimating and predicting behavior of such distributed parameter systems. The project will also develop a strongly integrated research, educational, and outreach program by providing graduate students with interdisciplinary and challenging research experiences, by providing undergraduate students with the opportunity of early involvement in research activities through algorithm development and test-bed experiments, and by motivating K-12 students by giving them hands-on experiences through university's Engineering Ambassadors(EA) program.The outcomes of this project are expected to advance the modeling, identification, and state estimation techniques for distributed parameter systems, offer comprehensive scientific understanding of the connections between DPS and mobile sensor networks, and contribute to generic engineering principles for designing cooperative control and distributed sensing strategies for mobile sensor networks. In particular, the work will: develop novel approaches for online parameter identification and state estimation of DPS using mobile sensor networks with reduced computational and communication cost compared to existing methods developed for static sensor networks; determine information-rich and energy-saving optimal trajectories of mobile sensor networks moving in DPS for simultaneously system identification and state prediction; and design a multi-robot test-bed with controllable advection-diffusion fields for the validation of the strategies, and conduct field experiments to test the strategies under realistic uncertainties and variations. The project also offers great educational opportunities for graduate, undergraduate, as well as K-12 students.