由信息与通信工程学院主办的“信通论坛”活动特别邀请Antonio教授作学术交流。具体安排如下，欢迎广大师生参加。
主讲人：Prof. 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教授
信通论坛（2025第9期）
主 题：Cognitive-Based ISAR Processing for Spectral Compatibility Applications
时 间：2025年7月7日16:20-17:20
地 点：清水河校区科研楼C216
报告简介：This lecture introduces and analyzes the concept of a cognitive inverse synthetic aperture radar (ISAR) ensuring spectral compatibility in crowded electromagnetic environments. In such a context, the proposed approach alternates between environmental perception, recognizing possible emitters in its frequency range, and an action stage, synthesizing and transmitting a tailored radar waveform to achieve the desired imaging task while guaranteeing spectral coexistence with overlaid emitters. The perception is carried out by a spectrum sensing module providing the true relevant spectral parameters of the sources in the environment. The action stage employs a tailored signal design process, synthesizing a radar waveform with bespoke spectral notches, enabling ISAR imaging over a wide spectral bandwidth without interfering with the other radio frequency (RF) sources. A key enabling requirement for the proposed application is the capability to successfully recover possible missing data in the frequency domain (induced by spectral notches) and in the slow-time dimension (enabling concurrent RF activities still in a cognitive fashion). This process is carried out by resorting to advanced methods based on either the compressed sensing framework or a rank-minimization recovery strategy. The capabilities of the proposed system are assessed exploiting a dataset of drone measurements in the frequency band between 13 GHz and 15 GHz. Results highlight the effectiveness of the devised architecture to enable spectral compatibility while delivering high-quality ISAR images as well as additional RF activities.
信通论坛（2025第10期）
主 题：Cognitive-Based ISAR Processing for Spectral Compatibility Applications
时 间：2025年7月8日16:10~15:40
地 点：清水河校区科研楼C218
报告简介：This lecture addresses the design of bespoke adaptive detectors for point-like targets embedded in a sea-clutter dominated environment using a multistatic/polarimetric radar network. The system consists of one monostatic as well as two collocated and cross-polarized bistatic sensors. The detector design accounts for possible range domain heterogeneity in sea-clutter backscattering, as well as potential functional relationships between the covariance matrices that characterize clutter returns across the bistatic polarimetric nodes. Accordingly, suitable estimates of the nuisance parameters for both monostatic and bistatic measurements are employed to develop adaptive decision rules based on the two-step Generalized Likelihood Ratio Test (GLRT) design criterion. The performance of the proposed receivers is assessed using simultaneously recorded monostatic and cross-polarized bistatic returns collected with the Netted RADar (NetRAD) system. The analysis assesses both the Constant False Alarm Rate (CFAR) behavior and the detection capability. The results show that, despite minor deviations from ideal CFAR behavior when near zero Doppler cells are tested, all the proposed decision rules maintain an overall robust CFAR behavior with respect to the nuisance parameters. In terms of detection capability, the proposed strategies outperform those relying solely on monostatic measurements and demonstrate comparable, or slightly improved, performance with respect to a competing approach confirming the effectiveness and robustness of the devised techniques.
信通论坛（2025第11期）
主 题：Off-Grid Multisnapshot Spectrum Sensing for Cognitive Radar
时 间：2025年7月9日10:30~12:00
地 点：清水河校区科研楼C218
报告简介：Spectrum sensing is a key aspect of next-generation cognitive radars that make use of the perception-action cycle to improve their performance while endowing cohabitation with other systems. Awareness of the electromagnetic (EM) environment surrounding the radar is demanded to adapt its behavior to the changing scene. 2-D spectrum sensing is usually carried-out on uniformly spaced grids, over which the angle of arrival (AOA) of diverse (unknown) sources is estimated along with their frequency occupancy. To mitigate the performance degradations of on-grid methods, this article proposes an off-grid 2-D profile recovery strategy where the atoms are no longer fixed according to a given pool of nominal AOAs, but some flexibility is allowed to infer off-grid angle displacements. Hence, the angle-frequency profile recovery process is formalized as a regularized maximum likelihood estimation capable of exploiting the inherent block-sparsity of the overall profile. The resulting challenging optimization problem is handled through a maximum block improvement (MBI)-based method, which provides an estimate of the three variable blocks involved in the process, viz., noise power, 2-D profile, and angular displacements. Furthermore, in order to enhance the reliability of determining the space-frequency occupancy map and accurately estimating the angle displacements, three refinement strategies for the 2-D spectrum profile are suggested, suitably leveraging Bayesian information criterion and false discovery rate paradigms. The proposed framework is then validated through numerical simulations in some realistic EM environments, also comparing the three proposed refinement strategies.
信通论坛（2025第12期）
主 题：X- and L-Band Radar Cross-Section Analysis of In-Flight UAVs at Different Polarizations
时 间：2025年7月9日14:30~16:00
地 点：清水河校区科研楼C218
报告简介：This lecture presents a comparative statistical analysis of in-flight Unmanned Aerial Vehicles (UAVs) Radar Cross-Section (RCS) in the X- and L-bands. The study is based on measurements
collected with a Frequency-Modulated Continuous-Wave (FMCW) radar in a rural area during the simultaneous flight of a commercial and a professional-grade drone. The objective is to assess how the statistical behavior of the RCS depends on radar frequency, polarization configuration, and drone type. The analysis is based on the evaluation of the Empirical Cumulative Distribution Function (ECDF) of the normalized RCS and its comparison with different theoretical models whose goodness of fit is assessed using the Cramer-von Mises (CVM) distance. In addition, dispersion metrics such as standard deviation, interquartile range, full range, and box-and-whisker plots are employed to provide a quantitative measure of the variability of the RCS under different operating conditions. The results show that, regardless
of the frequency band, each UAV conforms to a distinct RCS fluctuation model. Moreover, for a given UAV, the best fitting pattern changes depending on frequency and polarization. These variations reflect both the different structural characteristics of the UAVs and the frequency/polarization dependence of the RCS, as the scatterers that compose the drone may respond differently depending on the frequency/polarization.
主讲人简介：
Antonio De Maio (S'01-A'02-M'03-SM'07-F'13) was born in Sorrento, Italy, on June 20, 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".
