PECASE: Compressive Cooperative Sensing and Navigation in Mobile Networks

PECASE：移动网络中的压缩协作感知和导航

• 批准号: 1261614
• 负责人: Yasamin Mostofi
• 金额: $ 33.53万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2015-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1261614&HistoricalAwards=false
• 关键词: PECASE; Compressive; Cooperative; Sensing; Navigation

The research objective of this Faculty Early Career Development (CAREER) project is to develop the foundations of sensing and navigation in mobile cooperative networks from a compressive sampling perspective. A mobile ooperative network faces an abundance of information in its environment. Since there is not enough time for direct measurement of the whole terrain, finding the fundamental minimum sensing required for high-integrity and cooperative reconstruction of the parameter of interest is considerably important and an open problem. Currently, there is no analysis and design theory for ooperative mapping based on a severely under-determined data set. onsequently, a avigation framework that can guide the vehicles to the locations better for sparse sensing is also lacking. Inspired by the recent breakthroughs in non-uniform sampling theory, this proposal shows how the network can exploit the sparse transformation of the parameter of interest for cooperative mapping based on a considerably small observation set. The approach provides an answer to the question of "the next best positions for sensing" in mobile networks and guides the vehicles to the locations that are better for compressed sensing/mapping. To ensure uninterrupted cooperation, it furthermore complements this by proposing a foundation for communication-aware compressive mapping. Along this line, it shows how to build realistic communication objectives that are reflective of communication unreliability such as path loss, shadowing and fading, and integrate them with compressive sensing/mapping objectives. The research is fundamental in nature as it seeks to unveil the minimum sensing and communication needed for the robust operation of cooperative mobile networks. If successful, this research will make a significant contribution to the understanding and optimization of mobile cooperative networks in realistic communication environments. Emergency response, exploratory missions, security and surveillance are a few examples of the applications that have to operate in an information-rich environment robustly and in a timely manner, and can therefore benefit tremendously from the work.

该教师早期职业发展（职业）项目的研究目标是从压缩抽样的角度从移动合作网络中开发传感和导航的基础。一个移动的OPerative网络在其环境中面临大量信息。由于没有足够的时间直接测量整个地形，因此找到高融合性和合作重建感兴趣的参数所需的基本最低感知非常重要，这是一个开放的问题。当前，基于严重的数据集，尚无针对螺旋映射的分析和设计理论。随后，还缺乏一个可以指导车辆更好地引导车辆以进行稀疏感应的大型框架。受到最近非均匀抽样理论的突破的启发，该提案表明了网络如何基于相当小的观察集来利用感兴趣参数的稀疏转换，用于合作映射。该方法为移动网络中的“下一个最佳位置”的问题提供了答案，并将车辆引导到适合压缩感测/映射的位置。为了确保不间断的合作，它通过为通信感知抗压映射的基础提出基础来补充这一点。沿着这一行，它显示了如何构建反映通信不可靠的现实交流目标，例如路径丢失，阴影和褪色，并将它们与压缩感应/映射目标集成在一起。这项研究本质上是基本的，因为它试图揭示合作移动网络的强大操作所需的最低感知和沟通。如果成功，这项研究将为在现实的通信环境中对移动合作网络的理解和优化做出重大贡献。紧急响应，探索任务，安全和监视是必须及时及时在信息丰富的环境中运行的申请的一些示例，因此可以从工作中受益匪浅。