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.

What Is Automatic Target Recognition The capacity of an algorithm or device to detect targets or other objects based on data acquired from sensors is referred to as automatic target recognition, abbreviated as ATR. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Automatic target recognition Chapter 2: Computer vision Chapter 3: Radar Chapter 4: Synthetic-aperture radar Chapter 5: Beamforming Chapter 6: Pulse-Doppler radar Chapter 7: Inverse synthetic-aperture radar Chapter 8: Radar signal characteristics Chapter 9: Time delay neural network Chapter 10: Track algorithm (II) Answering the public top questions about automatic target recognition. (III) Real world examples for the usage of automatic target recognition in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of automatic target recognition' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of automatic target recognition.

What is Automatic Target Recognition The capacity of an algorithm or device to recognize targets or other objects based on data acquired from sensors is referred to as automatic target recognition, an abbreviation for these capabilities. How you will benefit (I) Insights, and validations about the following topics: Chapter 1: Automatic Target Recognition Chapter 2: Computer Vision Chapter 3: Radar Chapter 4: Doppler Radar Chapter 5: Synthetic-aperture Radar Chapter 6: Imaging Radar Chapter 7: Beamforming Chapter 8: Pulse-Doppler Radar Chapter 9: Passive Radar Chapter 10: Inverse Synthetic-aperture Radar (II) Answering the public top questions about automatic target recognition. (III) Real world examples for the usage of automatic target recognition in many fields. Who this book is for Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Automatic Target Recognition.

Radar Target Detection: Handbook of Theory and Practice covers a set of graphical solutions to the detection problem, designated as Meyer Plots, for radar systems design. A radar system's major purpose is the detection and location of an object by means of a return signal, which could be either a reflection or a beacon. This book is composed of four chapters. Chapter 1 presents the basic radar range equation, which is the basic relationship which permits the calculation of echo signal strength from measurable or known parameters of the radar transmitter, antenna, propagation path, and target. Chapter 2 provides examples for determining statistical signal detection using Meyer Plots, particularly for determining detection probability and integration loss and efficiency. Chapter 3 describes the application of target models to determination of detection probability. Chapter 4 examines how Meyer Plots can be used to solve problems involving most any target model.

This book examines the roles of sensors, physics–based attributes, classification methods, and performance evaluation in automatic target recognition. It details target classification from small mine–like objects to large tactical vehicles. Also explored in the book are invariants of sensor and transmission transformations, which are crucial in the development of low latency and computationally manageable automatic target recognition systems.