Abstract:

Compressive signal sampling is one of the recent important advances in signal processing and statistical learning, with impact to various applications including data sciences, communications, sensor networks, and medical imaging. It requires information-bearing signals to be sparse over known domains, either naturally or by design. In this talk, I will introduce the fresh notion of compressive covariance sensing, and advocate its exciting implications for (cyclo) stationary processes characterized by second-order statistical descriptors. Such descriptors include (periodic) covariances or frequency, cyclic, angular and Doppler spectra, which already effect signal compression even for non-sparse signals. Using this key observation, I will demonstrate how the attribute of sparsity can be bypassed, or leveraged more effectively, when recovering the second-order statistical information of interest. I will also delineate the minimal sampling rates for recovering certain useful statistics of non-sparse random signals, along with the compressive sampler designs for approaching such rates. I will illustrate the usefulness of compressive covariance sensing using several engineering applications that rely on frequency or angular spectrum sensing, such as wireless cognitive radio and statistical array processing.

Biography:

Zhi Tian received the B.E. degree from the University of Science and Technology of China, Hefei, China, in 1994, the M. S. and Ph.D. degrees from George Mason University, Fairfax, VA, in 1998 and 2000. Since August 2000, she has been on the faculty of Michigan Technological University, where she is currently a Professor. She is currently on leave to serve as a Program Director in the Division of Electrical, Communications and Cyber Systems at the US National Science Foundation. Her research interests lie in statistical signal processing, wireless communications and wireless sensor networks.  She is an IEEE Fellow. She served as Associate Editor for IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing.