一、主 题：Robust Estimation of Structured Covariance Matrices Under Practical Constraints
三、地 点：清水河校区 科研楼 B302
四、主讲人：宾夕法尼亚州立大学 Vishal Monga 副教授
五、主持人：信息与通信工程学院 崔国龙 教授
This talk will cover estimation problems under the umbrella of an exact rank constraint. The motivating scenario is statistical and radar signal processing focusing on the estimation of structured covariance matrices. In particular, we look at the regularized maximum likelihood (ML) estimation of structured covariance matrices (SCM) that arise in radar space time adaptive processing. The underlying physical model enables knowledge of the rank of the semi-definite component of the SCM which is a key constraint to incorporate. We will show that despite the presence of the challenging non-convex rank constraint, a closed form result that achieves the global optima is indeed possible. We also investigate scenarios where the knowledge of the rank may be imprecise and use expected likelihood approaches to determine the rank and other constraints of interest. Several uniqueness results in determining imprecise constraints are provided in this context.
Prof. Vishal Monga has been on the EE faculty at State since Fall 2009, where he is currently a tenured Associate Professor. From Oct 2005-July 2009 he was an imaging scientist with Xerox Research Labs. He has also been a visiting researcher at Microsoft Research in Redmond, WA and a visiting faculty at the University of Rochester. Prior to that, he received his PhDEE from the University of Texas at Austin in August 2005. Prof. Monga is a recipient of the National Science Foundation CAREER award. For his educational efforts, Dr. Monga received the 2016 Joel and Ruth Spira Teaching Excellence award. He is an Associate Editor for the IEEE Transactions on Image Processing and IEEE Signal Processing Letters, as well as an elected member of the IEEE Image, Video and Multidimensional Signal Processing (IVMSP) Technical Committee (2017-2019). Dr. Monga holds 45 granted US Patents.
编辑：王晓刚 / 审核：李果 / 发布者：陈伟