This book is based on the latest research on ISAR imaging of moving targets and non-cooperative target recognition (NCTR). It focuses on how to generate high-resolution ISAR images of targets of interest and how to deal with factors that affect the process. It also looks at extracting information from ISAR images and performing non-cooperative target recognition (NCTR) of moving targets.
Inverse Synthetic Aperture Radar Imaging covers the more detailed image formation and auto-focusing algorithms as well as applications of these algorithms to real world ISAR imaging. It also includes MATLAB source codes for the simulation of radar scattering from moving targets, implementations of ISAR image formation, auto-focusing, and imaging time selection, and simulations of bi-static and multi-static ISAR imaging algorithms.
Inverse Synthetic Aperture Radar Imaging provides readers with a working knowledge of the subject. Some key topics include: monostatic and bistatic RCS models for ISAR, point spread function and 2-D imaging, polarimetric ISAR, interferometry in ISAR, bandwidth extrapolation technique in ISAR, multi-window spectrum estimation, clean algorithm, effect of rotational motion on ISAR imaging, selection of optimal imaging timewindow, ISAR imaging in low SNR and in strong clutter, micro-Doppler features in ISAR, estimation of rotation in ISAR, multipath in ISAR, distortion analysis for bistatic ISAR, emulated bistatic ISAR, and multistatic ISAR.
This is essential reading for academics, graduates, and government and industry professionals. Both newer engineers and experts in radar should find this book of interest.
Supplementary material can be found at the IET's ebook page
Inverse Synthetic Aperture Radar Imaging: Principles, Algorithms, and Applications (Electromagnetics and Radar)
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Book Details
Author(s)Victor C. Chen, Marco Martorella
PublisherSciTech Publishing
ISBN / ASIN1613530137
ISBN-139781613530139
AvailabilityUsually ships in 24 hours
Sales Rank1,669,306
CategoryTechnology & Engineering
MarketplaceUnited States 🇺🇸
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