Non-Convex Optimization for Active & Passive Radar

This talk will present recent advances in passive and active multi-static radar from an optimization viewpoint; specifically non-convex optimization with the goal of designing provably exact and computationally efficient novel algorithms. A variety of challenging active and passive radar imaging problems encountered in real world and novel radar applications will be introduced some of which include passive and active imaging with limited bandwidth, limited number of measurements and unknown transmitted waveforms, imaging in the presence of phase errors and additive noise and clutter, imaging without phase information and multi-static interferometric imaging. The talk will motivate, describe and illustrate the application of convex and non-convex optimization principles in addressing these problems. The talk will introduce novel methods of low-rank matrix recovery and Wirtinger Flow and recent exciting applications in phaseless synthetic aperture radar, auofocus, passive radar, and super-resolution imaging.