Physics Of Automatic Target Recognition

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.

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.

"Integrates a broad range of physics, algorithms, and sensing techniques for development of intelligent systems. Highlights adaptive least-squared error modeling. Covers complex sampling, physical system modeling using digital filters, frequency domain processing, beamforming, and much more."

With the recent proliferation of service-oriented architectures (SOA), cloud computing technologies, and distributed-interconnected systems, distributed fusion is taking on a larger role in a variety of applications—from environmental monitoring and crisis management to intelligent buildings and defense. Drawing on the work of leading experts around the world, Distributed Data Fusion for Network-Centric Operations examines the state of the art of data fusion in a distributed sensing, communications, and computing environment. Get Insight into Designing and Implementing Data Fusion in a Distributed Network Addressing the entirety of information fusion, the contributors cover everything from signal and image processing, through estimation, to situation awareness. In particular, the work offers a timely look at the issues and solutions involving fusion within a distributed network enterprise. These include critical design problems, such as how to maintain a pedigree of agents or nodes that receive information, provide their contribution to the dataset, and pass to other network components. The book also tackles dynamic data sharing within a network-centric enterprise, distributed fusion effects on state estimation, graph-theoretic methods to optimize fusion performance, human engineering factors, and computer ontologies for higher levels of situation assessment. A comprehensive introduction to this emerging field and its challenges, the book explores how data fusion can be used within grid, distributed, and cloud computing architectures. Bringing together both theoretical and applied research perspectives, this is a valuable reference for fusion researchers and practitioners. It offers guidance and insight for those working on the complex issues of designing and implementing distributed, decentralized information fusion.