PECASE: Compressive Cooperative Sensing and Navigation in Mobile Networks

这项教师早期职业发展(CAEREAR)项目的研究目标是从压缩抽样的角度为移动协作网络中的传感和导航奠定基础。移动协作网络在其环境中面临着大量的信息。由于没有足够的时间直接测量整个地形，寻找高完整性和合作重建感兴趣参数所需的基本最小感知是相当重要的，也是一个开放的问题。目前，还没有基于严重欠确定数据集的操作映射的分析和设计理论。因此，也缺乏一种能够更好地引导车辆到达稀疏感知位置的导航框架。受非均匀抽样理论最新突破的启发，该方案展示了网络如何利用感兴趣参数的稀疏变换来基于相当小的观测集进行合作映射。该方法回答了移动网络中“下一个最佳感知位置”的问题，并将车辆引导到更适合压缩感知/地图绘制的位置。为了确保不间断的合作，它还提出了通信感知压缩映射的基础，从而进一步补充了这一点。沿着这条线，它展示了如何建立反映通信不可靠性的现实通信目标，例如路径损耗、阴影和衰落，并将它们与压缩感知/映射目标相结合。这项研究本质上是基础的，因为它试图揭示合作移动网络稳健运行所需的最低限度的感知和通信。如果研究成功，将对现实通信环境下移动协作网络的理解和优化做出重要贡献。应急响应、探索性任务、安全和监视是必须在信息丰富的环境中稳健和及时地运行的应用程序的几个例子，因此可以从这项工作中获得巨大好处。

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.