学　术

　　本次人力资源部“名师讲堂”活动邀请到IEEE Fellow、意大利那不勒斯费德里克二世大学Antonio De Maio教授来校交流。具体安排如下，欢迎师生们参加。

　　报告一：

　　主　题：Detection of Multiple Noise-like Jammers for Radar Applications

　　时　间：2018年7月17日（周二）14:30-15:20

　　地　点：清水河校区科研楼B302

　　主讲人：Antonio De Maio （IEEE Fellow，University of Naples Federico II）

　　主持人：信息与通信工程学院 崔国龙　研究员

　　内容简介：

　　Model Order Selection (MOS) rules are exploited to devise two adaptive architectures for multiple noise-like jammer detection which process clutter-free data. Specifically, the former contains a MOS-based stage, that makes inference on the number of jammers, and a detection stage, that confirms the presence of the jammers suitably exploiting the estimates provided by the first stage. On the other hand, the latter architecture, which relies on the Modified Likelihood Ratio Test, jointly performs detection and estimation. Remarkably, both guarantee the constant false alarm rate property with respect to the thermal noise power. At the analysis stage, the performance of the proposed architectures are investigated highlighting the interplay among the different parameters.

　　报告二：

　　题　目：A Robust Framework for Covariance Classification in Heterogeneous Polarimetric SAR Images

　　时　间：2018年7月17日（周二）15:30

　　地　点：清水河校区科研楼B302

　　主讲人：Antonio De Maio （IEEE Fellow，University of Naples Federico II）

　　内容简介：

　　An automatic classification approach for polarimetric covariance structure is introduced and assessed. It refers to the heterogeneous environment, where the pixels of the polarimetric image share the same covariance structure but different power levels. The Principle of Invariance is exploited to replace original data with a suitable statistic whose distribution is independent of the scale factors. Then, the classification problem is formulated in terms of a multiple hypotheses test and solved by means of model order selection rules. The behavior of the newly devised classifiers is first assessed over simulated data also in comparison with the analogous counterparts for homogeneous environment. Next, the classification performances are evaluated on real measured data corroborating the excellent results highlighted in the simulations.

　　主讲人简介：

　　Antonio De Maio was born in Sorrento, Italy, 1974. He received the Dr.Eng. degree (with honors) and the Ph.D. degree in information engineering, both from the University of Naples Federico II, Naples, Italy, in 1998 and 2002, respectively. From October to December 2004, he was a Visiting Researcher with the U.S. Air Force Research Laboratory, Rome, NY. From November to December 2007, he was a Visiting Researcher with the Chinese University of Hong Kong, Hong Kong. Currently, he is a Professor with the University of Naples Federico II. His research interest lies in the field of statistical signal processing, with emphasis on radar detection and optimization theory applied to radar signal processing. Dr. De Maio is a Fellow member of IEEE and the recipient of the 2010 IEEE Fred Nathanson Memorial Award as the young (less than 40 years of age) AESS Radar Engineer 2010 whose performance is particularly noteworthy as evidenced by contributions to the radar art over a period of several years, with the following citation for "robust CFAR detection, knowledge-based radar signal processing, and waveform design and diversity".

　　主办单位：人力资源部教师发展中心

　　承办单位：信息与通信工程学院

　　　　　　　　　　　　　　　　　　　　人力资源部教师发展中心

　　　　　　　　　　　　　　　　　　　　　　2018年7月16日