Abstract
The modeling of the clutter echoes is a central issue for the design and performance evaluation of radar systems. Aim of this lecture is to describe the state-of-the-art approaches to the modeling and understanding of non-Gaussian radar clutter echoes and their implications on performance prediction and signal processors design.
After a short first part dedicated to modern statistical and spectral models for high-resolution sea and ground clutter and to the methods of experimental validation using recorded data sets, the lecture will focus on coherent radar detection in non-Gaussian background.
In high-resolution radar systems, the disturbance cannot be modeled as Gaussian distributed and the classical detectors suffer from high losses. Then, according to the adopted disturbance model, optimum and sub-optimum detectors are derived and their performance analyzed against a non-Gaussian background. Different interpretations of the various detectors are provided that highlight the relationships and the differences among them. Moreover, some discussion is dedicated to how to make adaptive the detectors, by incorporating a proper estimate of the disturbance covariance matrix, in order to guarantee the CFAR behaviour of the detector.
A plethora of results with simulated and real recorded data will be shown.
Biography
Maria S. 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 Associate Professor since December 2011. She’s IEEE fellow since January 2011 and she was co-recipient of the 2001 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.
