Biography: Athina P. Petropulu received her undergraduate degree from the National Technical University of Athens, Greece, and the M.Sc. and Ph.D. degrees from Northeastern University, Boston MA, all in Electrical and Computer Engineering. Since 2010, she is Professor and Chair of the Electrical and Computer Engineering Department at Rutgers, The State University of New Jersey. Before that she was faculty at Drexel University. Dr. Petropulu's research interests span the area of statistical signal processing, wireless communications, signal processing in networking, physical layer security, and radar signal processing. Her research has been funded by various government industry sponsors including the National Science Foundation, the Office of Naval research, the US Army, the National Institute of Health, the Whitaker Foundation, Lockheed Martin.

Dr. Petropulu is Fellow of IEEE and recipient of the 1995 Presidential Faculty Fellow Award given by NSF and the White House. She has served as Editor-in-Chief of the IEEE Transactions on Signal Processing (2009-2011).

IEEE Signal Processing Society Vice President-Conferences(2006-2008), and member-at-large of the IEEE Signal Processing Board of Governors. She was the General Chair of the 2005 International Conference on Acoustics Speech and Signal Processing (ICASSP-05), Philadelphia PA.

In 2005 she received the IEEE Signal Processing Magazine Best Paper Award, and in 2012 the IEEE Signal Processing Society Meritorious Service Award for "exemplary service in technical leadership capacities".

Title: MIMO Radar based on Sparse Sensing and Matrix Completion
Abstract: In both civilian and military applications, there is increasing interest in networked radars that are cheap and have small form factors, yet they enable reliable surveillance of an area and provide actionable situation awareness. Unfortunately, these requirements are competing in nature. A networked radar is a configuration of transmit and receive antennas. The transmit antennas transmit probing waveforms. By jointly processing the signals from all receive antennas the desired target parameters can be extracted. This processing can be done at a fusion center, which collects the measurements of all receive antennas. Reliable surveillance requires collection, communication and fusion of vast amounts of data from various antennas, the communication of which is power consuming. Radars that can be placed on the nodes of a wireless sensor network are also of interest. In that case, the communication with the fusion center would occur via a wireless link. Multiple-input multiple-output (MIMO) radars have received considerable recent attention as they can achieve superior resolution. Recently, our group proposed compressive sensing (CS) based MIMO radars, which can achieve the same resolution as MIMO radars but with significantly fewer data samples, or significantly higher resolution with the same number of samples. CS-MIMO radar exploit the sparsity of the targets in the angle-range-speed space. In order to achieve their high resolution, they discretize the target space on a fine grid, however, errors may occur when the targets fall between grid points. The talk will present a new networked MIMO radar system that relies on sparse sensing and matrix completion techniques (MC-MIMO radar), in order to achieve an optimal tradeoff between the competing requirements of reliability and cost. MC-MIMO radars achieve “super-resolution'' in the angle, Doppler and range space, while limiting the amount of data measured and transmitted by each sensor through the network. Unlike CS-MIMO radars they do not require grid discretization. The talk will also present theoretical results on target recoverability and performance guarantees for matrix completion applied to MIMO radars.