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.
