Integrative, Hybrid and Complex Systems Edwin A. Marengo Northeastern University CAREER: An Interdisciplinary Approach to the Study of Wave-Based Signal Processing: Compressive Sensing and Signal-Subspace-Based Imaging
Intellectual Merit: This research studies wave-based systems, their signals, and processing. The research is carried out within the particular and comparative framework provided by two signal processing approaches, compressive sensing and signal subspace methods. Compressive sensing is emerging as a promising new approach to simultaneously and non-adaptively sample and compress sparse signals. Signal subspace methods form a broad class of super-resolution approaches whose applicability to imaging of complex targets has been studied by the principal investigator. The goal of this project is to study the nascent compressive sensing approach and the better established signal subspace approach in a synergistic framework motivated by open problems in active detection and super-resolution imaging. Most past work in compressive sensing has focused on passive sensing and linear systems, while this project focuses on active sensing. Here the inverse problem is generally nonlinear. Thus, this project aims to extend compressive sensing to active sensing and nonlinear systems. Moreover, past work has focused on compressive estimation, while this research will also address compressive detection. The program includes an experimental validation component in the form of an active optical compressive sensing test bed. The test bed will be used for target detection, imaging and wireless communications.
Broader Impact: Specific educational activities of this proposal include: 1) on-going and future undergraduate research in the investigator?s laboratory; 2) the development of an undergraduate capstone project pipeline; 3) the development of a new undergraduate translational research co-op course which benefits from Northeastern University?s successful cooperative education program; and 4) the publications and conference papers co-authored by the investigator and his students including conferences in undergraduate research and pedagogy. The results on compressive sensing and signal-subspace-based imaging deriving from this research have the potential to dramatically impact biomedical imaging, homeland security sensing, nondestructive testing (NDT) and other areas since they are expected to enable better detection and super-resolution imaging performance with limited data in comparison with existing approaches.
