Innovative Framework for Real-Time Estimation of Threats Using Mobile Sensor Network

该项目将建立一个数学框架，通过安装在车辆上的气体浓度传感器网络收集的数据，实时描述不断变化的情况。这有可能评估火灾的发展、危险化学品的泄漏和危险气体的释放，甚至河流和河口盐度和氧气的变化。所提出的方法的创新属性是将对威胁的估计与携带传感器的车辆网络的引导和运动相结合。这一理论和算法框架也可以在制定疏散和搜救任务方面发挥关键作用，从而促进国民的健康和福利需求。该项目将通过为研究生和本科生团队提供多学科领域的研究机会来促进教和学。该项目团队将对由异质移动传感器网络组成的分布式动态环境进行测量、控制和分析的研究。移动车辆传感器携带车载气体浓度传感器，部署在固定或可能移动的气体源产生时空演变的威胁场的空间区域。分布式动态环境中的源定位和状态重构将利用地形移动车辆的异质网络--传感器，其制导和编队控制由空间分布过程的物理决定。这项研究将产生新的植物理论状态估计器，描述时空变化场的演变，增强互联车辆-传感器的操作、运动和导航。传感器技术的局限性被基于性能的车辆编队运动-传感器和车辆制导从信息敏感到信息厌恶的转变所绕过。时空变化的场估计方法将通过使用地形车辆-传感器和气体源的不同网络的实验来验证。该项目具有广泛的应用，并为学生提供了一个极好的学习机会。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。

This project will establish a mathematical framework that will allow real-time characterization of evolving conditions that can be detected through data collected by networks of gas concentration sensors mounted on vehicles. This has the potential to evaluate development of fires, hazardous chemical spills and release of hazardous gases, and even changes in the salinity and oxygen in rivers and estuaries. The innovative attribute of the proposed methodology is the integration of the estimation of the threats with the guidance and motion of the network of vehicles that are carrying the sensors. This theoretical and algorithmic framework can also be pivotal in development of evacuation and of search and rescue missions, thereby advancing national health and welfare needs. This project will promote teaching and learning by providing teams of graduate and undergraduate students with research opportunities in multi-disciplinary areas. The project team will perform research on measurement, control and analysis of a distributed dynamic environment comprised of a heterogeneous mobile sensor network. The mobile vehicles-sensors carry onboard gas concentration sensors and are deployed in a spatial area where a stationary or possibly a moving gaseous source generates a spatiotemporally evolving threat field. A source localization and state reconstruction in distributed dynamic environments will utilize a heterogeneous network of terrain mobile vehicles-sensors whose guidance and formation control is dictated by the physics of the spatially distributed process. The research will result in new theoretical state estimators of plants describing the evolution of spatiotemporally varying fields that augment the operation, motion and guidance of connected vehicles-sensors. The limitations of sensor technologies are circumvented by a performance-based formation motion of the vehicles-sensors and a transition of the vehicle guidance from information-sensitive to information-averse. The spatiotemporally varying field estimation approach will be validated with an experiment using a heterogeneous network of terrain vehicles-sensors and a gaseous source. The project has wide-spread applications and offers an excellent learning opportunity for students.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.