课程号：EE206002

课程名：雷达检测理论

英文课程名：Radar detection theory

开课单位：电子工程学院

授课教师：Maria Greco

责任教师：严俊坤  

学分/学时：1学分/16学时

教师简介：1993年Maria Greco毕业于意大利比萨大学电子工程专业，1998年获得电信工程博士学位。1997年12月至1998年5月，她以访问学者的身份加入美国亚特兰大佐治亚理工学院，从事非高斯背景下雷达探测、雷达信号处理领域的研究活动。1993年加入比萨大学信息工程系，2016年12月起任教授。她于2011年1月成为IEEE fellow，并于2001年和2012年分别获得IEEE航空航天和电子系统学会颁发的最佳论文奖和2008年Fred Nathanson青年工程师奖，以表彰她对信号估计和检测理论的贡献 。   Maria Greco graduated in Electronic Engineering in 1993 and received the Ph.D. degree in Telecommunication Engineering in 1998, from University of Pisa, Italy. From December 1997 to May 1998 she joined the Georgia Tech Research Institute, Atlanta, USA as a visiting research scholar where she carried on research activity in the field of radar detection in non-Gaussian background. In 1993 she joined the Department of Information Engineering of the University of Pisa, where she is Full Professor since December 2016. She’s IEEE fellow since January 2011 and she was co-recipient of the 2001 and 2012 IEEE Aerospace and Electronic Systems Society’s Barry Carlton Award for Best Paper and recipient of the 2008 Fred Nathanson Young Engineer of the Year award for contributions to signal processing, estimation, and detection theory

课程简介：本课程主要介绍雷达检测的基本理论和方法，包括检测问题介绍、重要概率密度函数的总结和蒙特卡洛性能评估、雷贝叶斯风险和最大后验检测器、接收机特性、高斯白噪声下的信号检测、贝叶斯方法和广义似然比检测。希望通过对本课程的学习，学生可以了解雷达信号检测所必需的基本信号处理方法的原则；评估雷达信号检测算法的性能和局限性。从而为先进雷达技术的研究奠定坚实的基础。

This course mainly introduces the basic theories and methods of radar detection, including Introduction to the detection problem, Summary of important PDFs and Monte Carlo Performance Evaluation, Bayes risk and MAP detector, Receiver operating characteristics,

Signal detection in Gaussian white noise, and Composite Hypothesis Testing: Bayesian approach and Generalized

Likelihood Ratio Test (GLRT). We hope that students who choose this course will benefit: Learn the principles and of radar detection methods; Evaluate radar signal detection algorithm performance and limitations. Develop a strong foundation for study of advanced radar techniques.

先修课程：概率论，信号与系统，数字信号处理