Radar Automatic Target Recognition Atr And Non Cooperative Target Recognition Nctr

Radar Automatic Target Recognition (ATR) and NonCooperative Target Recognition (NCTR) captures material presented by leading international experts at a NATO lecture series and explores both the fundamentals of classification techniques applied to data from a variety of radar modes and selected advanced techniques at the forefront of research. The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge, although steady progress has been made over the past couple of decades. This book explores both the fundamentals of classification techniques applied to data from a variety of radar modes and selected advanced techniques at the forefront of research. Topics include: the problem as applied to the ground, air and maritime domains; impact of image quality on the overall target recognition performance; performance of different approaches to the classifier algorithm; improvement in performance to be gained when a target can be viewed from more than one perspective; ways in which natural systems perform target recognition; impact of compressive sensing; advances in change detection, including coherent change detection; and challenges and directions for future research.

Contains the unclassified papers presented at the Symposium. Novel solutions to the Non-Cooperative Target Identification (NCTI) Problem, using radar are proposed. The papers are presented under the following headings: System requirements -- Target characterisation -- Radar measurements and feature extraction -- Target classification -- Scattering techniques, target modelling and validation.

Authoritative reference on the state of the art in the field with additional coverage of important foundational concepts Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning presents cutting-edge research advances in the rapidly growing areas in optical and RF electromagnetic device modeling, simulation, and inverse-design. The text provides a comprehensive treatment of the field on subjects ranging from fundamental theoretical principles and new technological developments to state-of-the-art device design, as well as examples encompassing a wide range of related sub-areas. The content of the book covers all-dielectric and metallodielectric optical metasurface deep learning-accelerated inverse-design, deep neural networks for inverse scattering, applications of deep learning for advanced antenna design, and other related topics. To aid in reader comprehension, each chapter contains 10-15 illustrations, including prototype photos, line graphs, and electric field plots. Contributed to by leading research groups in the field, sample topics covered in Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning include: Optical and photonic design, including generative machine learning for photonic design and inverse design of electromagnetic systems RF and antenna design, including artificial neural networks for parametric electromagnetic modeling and optimization and analysis of uniform and non-uniform antenna arrays Inverse scattering, target classification, and other applications, including deep learning for high contrast inverse scattering of electrically large structures Advances in Electromagnetics Empowered by Artificial Intelligence and Deep Learning is a must-have resource on the topic for university faculty, graduate students, and engineers within the fields of electromagnetics, wireless communications, antenna/RF design, and photonics, as well as researchers at large defense contractors and government laboratories.

"From an engineer designing Automatic Target Recognition (ATR) systems for 40 years, comes this step-by-step guide to producing state-of-the-art ATR systems. The full spectrum of ATR designs are covered, from systems that just suggest targets to the warfighter to ATRs that could serve as the "brains" of lethal autonomous robots. Unfortunately, when it comes to ATR, some practitioners claim that their off-the-shelf canned algorithms magically leap from academic research to deployment with scant domain knowledge or system engineering. Deep learning is marketed more than deep understanding, deep explainability or deep fusion of on-platform resources. Naïve practitioners twist a few algorithmic knobs, and test on data of uncertain virtue, until performance seems superb. Unfortunately, with the enemy and ever changing environment conspiring to defeat detection and recognition, naively designed ATRs can fail in unexpected and spectacular ways. Trustworthy ATRs need to fuse multiple data and metadata sources, continuously learn from and adapt to their environment, interact with humans in natural language, and deal with in-library and out-of-library targets and confusor objects. This book provides a blueprint for smarter, more autonomous, more sophisticated ATR designs"--

This authoritative resource presents a comprehensive illustration of modern Artificial Intelligence / Machine Learning (AI/ML) technology for radio frequency (RF) data exploitation. It identifies technical challenges, benefits, and directions of deep learning (DL) based object classification using radar data, including synthetic aperture radar (SAR) and high range resolution (HRR) radar. The performance of AI/ML algorithms is provided from an overview of machine learning (ML) theory that includes history, background primer, and examples. Radar data issues of collection, application, and examples for SAR/HRR data and communication signals analysis are discussed. In addition, this book presents practical considerations of deploying such techniques, including performance evaluation, energy-efficient computing, and the future unresolved issues.

This book text provides an overview of the radar target recognition process and covers the key techniques being developed for operational systems. It is based on the fundamental scientific principles of high resolution radar, and explains how the underlying techniques can be used in real systems, taking into account the characteristics of practical radar system designs and component limitations. It also addresses operational aspects, such as how high resolution modes would fit in with other functions such as detection and tracking.

Over 3.400 total pages ... Includes: Electronic Warfare and Radar Systems Engineering Handbook, 2013, 455 pages Electronic Warfare and Radar Systems Engineering Handbook, 2012, 399 pages Electronic Warfare and Radar Systems Engineering Handbook, 1999, 287 pages Electronic Warfare and Radar Systems Engineering Handbook, 1997, 602 pages Electronic Warfare Fundamentals, 2000, 351 pages Radar Fundamentals Student Guide Volume II, no date, 355 pages Principles of Naval Weapons Systems, no date, 351 pages Electronic Warfare, U.S. Marine Corps, 2002, 73 pages Marine Corps Warfighting Publication (MCWP) 6-22, Communications and Information Systems, 1999, 146 pages Marine Corps Warfighting Publication (MCWP) 6-22D, Field Antenna Handbook, 1999, 146 pages, 192 pages Plan / Design / Layout Of Satellite Communication Systems, 1994, 169 pages