Helmholtz meets Heisenberg: Sparse Remote Sensing
Prof. Thomas Strohmer, Department of Mathematics University of California, Davis

Seminar • 05 May 2009 • CO 017

Abstract
We consider the problem of detecting targets via remote sensing. This imaging problem is typically plagued by nonuniqueness and instability and hence mathematically challenging. Traditional methods such as matched field processing are rather limited in the number of targets that they can reliably recover at high resolution. By utilizing sparsity and tools from compressed sensing I will present methods that significantly improve upon existing radar imaging techniques. I will derive fundamental performance and resolution limits for compressed radar imaging with respect to the number of sensors and resolvable targets. These theoretical results demonstrate the advantages as well as limitations of compressed remote sensing. Numerical simulations confirm the theoretical analysis. This is joint work with Albert Fannjiang, Mike Yan, and Matt Herman.