Biograph: Marco Martorella received his Laurea degree (Bachelor+Masters) in Telecommunication Engineering in 1999 (cum laude) and his PhD in Remote Sensing in 2003, both at the University of Pisa. He is now an Associate Professor at the Department of Information Engineering of the University of Pisa where he lectures “Fundamentals of Radar” and “Digital Communications” and an external Professor at the University of Cape Town where he lectures “High Resolution and Imaging Radar” within the “Masters in Radar and Electronic Defence”. He is author of about 170 international journal and conference papers, three book chapters and a book entitled “Inverse Synthetic Aperture Radar Imaging: Principles, Algorithms and Applications”. He has presented several tutorials at international radar conferences and organised a special issue on Inverse Synthetic Aperture Radar for the Journal of Applied Signal Processing. He is a member of the IET Radar Sonar and Navigation Editorial Board, a senior member of the IEEE and a member of AFCEA. He is currently the chair of the NATO-awarded research task group NATO SET-196 on “Multichannel/Multistatic radar imaging of non-cooperative targets” and co-chair of NATO SET-236 on “Robust compressive sensing techniques for radar and ESM applications”. He was also chair of the specialist meeting NATO SET-228 on “Radar Imaging for Target Identification”. He has been recipient of the 2008 Italy-Australia Award for young researchers, the 2010 Best Reviewer for the IEEE GRSL, the IEEE 2013 Fred Nathanson Memorial Radar Award and the 2017 NATO SET Panel Excellence Award. He is co-founder of a radar systems-related spin-off company, namely ECHOES. His research interests are mainly in the field of radar imaging and multichannel signal processing.

Title: Introduction to Inverse Synthetic Aperture Radar

Abstract: Inverse Synthetic Aperture Radar (ISAR) is a technique used for reconstructing radar images of moving targets. Often, modern high-resolution radars implicitly offer the system requirements needed for implementing ISAR imaging. ISAR images can be obtained by means of a signal processing that can be enabled both on and off-line by using dedicated image formation algorithms. Automatic Target Recognition (ATR) systems are often based on the use of radar images because they provide a 2D e.m. map of the target reflectivity. Therefore, classification features that contain spatial information can be extracted and used to increase the performance of classifiers. The understanding of ISAR image formation is crucial for optimizing ATR systems that are based on such images.
Description: This tutorial aims at providing an introduction to ISAR. The lecture is divided in three parts: the first part deals with principles of ISAR, the second part concerns ISAR processing and the third part focuses on advanced ISAR systems, such as bistatic, passive and multistatic ISAR systems. The ISAR system is introduced by defining the radar-target geometry and by considering simple radar concepts. The derivation of the ISAR processor is obtained by defining the signal model and by interpreting it in the Fourier domain. Differences between ISAR and SAR are also highlighted in order to better understand ISAR concepts.Basic and advanced techniques are presented in order to provide an overview of the current methods used for implementing ISAR and improving its performance. In particular, the problem of ISAR image autofocus is analysed in details and several solutions are presented. Bistatic and multistatic ISAR will also be introduced together with suitable ISAR techniques that aim at forming bistatic and multistatic ISAR images.Several examples with simulations and real data are provided throughout the tutorial in order to demonstrate the effectiveness and potentiality of ISAR imaging. A list of this tutorial contents follows.
1. Introduction
1.1. Synthetic Aperture Radar (SAR)
1.2. Inverse Synthetic Aperture Radar (ISAR)
1.3. ISAR system
1.4. Examples of applications
2. Signal modelling
2.1. Radar-target geometry
2.2. Transmitted signal
2.3. Received signal (Time-Frequency representation)
2.4. Radial motion compensation
2.5. Interpretation of the received signal in the Fourier Domain
3. ISAR image reconstruction
3.1. Image formation
3.2. Point Spread Function (PSF)
3.3. Image Resolution
3.4. Analogies and differences with SAR
4. ISAR image Autofocus
4.1. Image Contrast Based Autofocus (ICBA)
4.2. Image Entropy Based Autofocus (IEBA)
5. Time window selection
5.1. Max Image Contrast (IC) method
6. Examples of ISAR applications