Download or read book Characterization of Deep Neural Network Feature Space for Inverse Synthetic Aperture Radar Automatic Target Recognition written by Christopher Z. Au and published by . This book was released on 2020 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Airborne Radar Systems and Techniques group at MIT Lincoln Laboratory trained neural networks to classify different targets at sea based on inverse synthetic aperture radar (ISAR) data. Simulated data was used to train these neural network based automatic target recognition (ATR) systems. The technical challenge of this project was to find a way to evaluate the quality and adequacy of a limited set of training data. Using simulated ISAR images to train neural networks, the project determined the minimum amount of variation in terms of parameters such as aspect angle to adequately train a neural network. Establishing a correspondence between training data variation and the resulting feature space of the data informed the minimum spanning-set of training data required for future data collects.
Download or read book Deep Learning for Radar and Communications Automatic Target Recognition written by Uttam K. Majumder and published by Artech House. This book was released on 2020-07-31 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Automatic Target Recognition of Ground Vehicles Using Synthetic Aperture Radar with Statistical and Neural Network Approaches to Classification written by Kevin Michael Olson and published by . This book was released on 1996 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Radar Automatic Target Recognition ATR and Non Cooperative Target Recognition NCTR written by David Blacknell and published by IET. This book was released on 2013-08-23 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Deep Neural Network Design for Radar Applications written by Sevgi Zubeyde Gurbuz and published by SciTech Publishing. This book was released on 2020-12-31 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Novel deep learning approaches are achieving state-of-the-art accuracy in the area of radar target recognition, enabling applications beyond the scope of human-level performance. This book provides an introduction to the unique aspects of machine learning for radar signal processing that any scientist or engineer seeking to apply these technologies ought to be aware of.
Download or read book Automatic Target Recognition written by and published by . This book was released on 1997 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms written by Caner Ozdemir and published by John Wiley & Sons. This book was released on 2021-03-22 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build your knowledge of SAR/ISAR imaging with this comprehensive and insightful resource The newly revised Second Edition of Inverse Synthetic Aperture Radar Imaging with MATLAB Algorithms covers in greater detail the fundamental and advanced topics necessary for a complete understanding of inverse synthetic aperture radar (ISAR) imaging and its concepts. Distinguished author and academician, Caner Özdemir, describes the practical aspects of ISAR imaging and presents illustrative examples of the radar signal processing algorithms used for ISAR imaging. The topics in each chapter are supplemented with MATLAB codes to assist readers in better understanding each of the principles discussed within the book. This new edition incudes discussions of the most up-to-date topics to arise in the field of ISAR imaging and ISAR hardware design. The book provides a comprehensive analysis of advanced techniques like Fourier-based radar imaging algorithms, and motion compensation techniques along with radar fundamentals for readers new to the subject. The author covers a wide variety of topics, including: Radar fundamentals, including concepts like radar cross section, maximum detectable range, frequency modulated continuous wave, and doppler frequency and pulsed radar The theoretical and practical aspects of signal processing algorithms used in ISAR imaging The numeric implementation of all necessary algorithms in MATLAB ISAR hardware, emerging topics on SAR/ISAR focusing algorithms such as bistatic ISAR imaging, polarimetric ISAR imaging, and near-field ISAR imaging, Applications of SAR/ISAR imaging techniques to other radar imaging problems such as thru-the-wall radar imaging and ground-penetrating radar imaging Perfect for graduate students in the fields of electrical and electronics engineering, electromagnetism, imaging radar, and physics, Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms also belongs on the bookshelves of practicing researchers in the related areas looking for a useful resource to assist them in their day-to-day professional work.
Download or read book The Evaluation of Synthetic Aperture Radar Image Segmentation Algorithms in the Context of Automatic Target Recognition written by and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Image segmentation is a process to extract and organize information energy in the image pixel space according to a prescribed feature set. It is often a key preprocess in automatic target recognition (ATR) algorithms. In many cases, the performance of image segmentation algorithms will have significant impact on the performance of ATR algorithms. Due to the variations in feature set definitions and the innovations in the segmentation processes, there is large number of image segmentation algorithms existing in the ATR world. The problem is which image segmentation algorithm performs best for an ATR application. There are a number of measures to evaluate the performance of segmentation algorithms, such as Percentage Pixels Same (pps), Partial Directed Hausdorff (pdh), and Complex Inner Product (cip). In the research, we found that the combination of the three measures shows effectiveness in the evaluation of segmentation algorithms against truth data (human master segmentation). However, we don't know what are the impact of those measures in the performance of ATR algorithms that are commonly measured by Probability of detection (PDet), Probability of false alarm (PFA), Probability of identification (PID), etc. In all practical situations, ATR boxes are implemented without human observer in the loop. The performance of synthetic aperture radar (SAR) image segmentation should be evaluated in the context of ATR rather than human observers.
Download or read book Automatic Target Recognition for Synthetic Aperture Radar Using Multiple Features written by Chia-Huei Yao and published by . This book was released on 2005 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: A project to identify various military vehicles in the Moving and Stationary Target Acquisitions and Recognition (MSTAR) image repository. The collecting and sorting was previously accomplished, so this study focuses on analysis and identification of images. The project was divided into three stages: image filtration, feature extraction, and target classification. This thesis concentrated on stages one and two. Alvin Wang, another graduate student, emphasized the third stage in his thesis.
Download or read book Automatic Target Recognition for Synthetic Aperture Radar Imagery Data Using Hausdorff Distance Transform written by Alvin Wang and published by . This book was released on 2004 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic Target Recognition (ATR) is a pattern recognition technique used to detect targets in a defined area of interest. The objective of the research was to discover the potential of applying ATR to Synthetic Aperture Radar (SAR) images and to explore the opportunities of employing Hausdorff Distance Transform (HDT) on multiple feature sets.
Download or read book New Feature Extraction and Matching Algorithms for Automatic Target Recognition in Synthetic Aperture Radar written by Adam T. Hipp and published by . This book was released on 2006 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: A project to develop an automatic target recognition (ATR) algorithm for synthetic aperture radar (SAR) imagery data, matching an unknown target to one of the known reference targets based on a maximum likelihood estimation procedure. Central to the algorithm is the CLEAN method, which tries to strengthen peak feature image classification importance. The effectiveness of the CLEAN algorithm will be assessed by comparing target recognition accuracy of CLEANed images to those that have not undergone the CLEAN method.
Download or read book Space Object Identification Using Feature Space Trajectory Neural Networks written by Neal W. Bruegger and published by . This book was released on 1997-03-01 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Feature Space Trajectory Neural Network (FSTNN) is a simple yet powerful pattern recognition tool developed by Neiberg and Casasent for use in an Automatic Target Recognition System. Since the FSTNN was developed, it has been used on various problems including speaker identification and space object identification. However, in these types of problems, the test set represents time series data rather than an independent set of points. Since the distance metric of the standard FSTNN treats each test point independently without regard to its position in the sequence, the FSTNN can yield less than optimal results in these problems. Two methods for incorporating sequence information into the FSTNN algorithm are presented. These methods, Dynamic Time Warping (DTW) and Uniform Time Warping (UTW), are described and compared to the standard FSTNN performance on the space object identification problem. Both reduce error induced by improper synchronization of the test and training sequences and make the FSTNN more generally applicable to a wide variety of pattern recognition problems. They incorporate sequencing information by synchronizing the test and training trajectories. DTW accomplishes this 'on-the-fly' as the sequence progresses while UTW uniformly compensates for temporal differences across the trajectories. These algorithms improve the maximum probability of false alarm (PFA) of the standard FSTNN by an average of 10.18% and 27.69%, respectively, although UTW is less consistent in its results. A metric for determining the saliency of the features in an FSTNN is also presented and demonstrated.
Download or read book Teorija na poznanieto kritika na idealizma istorija na naukata written by and published by . This book was released on 1973 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book High frequency Autofocus Algorithm for Noncooperative ISAR written by Geoffrey H. Goldman and published by . This book was released on 1999 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applications of Artificial Neural Networks in Image Processing written by and published by . This book was released on 1998 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Target Recognition written by Bruce Jay Schachter and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This third edition of Automatic Target Recognition provides a roadmap for breakthrough ATR designs with increased intelligence, performance, and autonomy. Clear distinctions are made between military problems and comparable commercial Deep Learning problems. These considerations need to be understood by ATR engineers working in the defense industry as well as by their government customers. A reference design is provided for a next-generation ATR that can continuously learn from and adapt to its environment. The convergence of diverse forms of data on a single platform supports new capabilities and improved performance. This third edition broadens the notion of ATR to multisensor fusion. Radical continuous-learning ATR architectures, better integration of data sources, well-packaged sensors, and low-power teraflop chips will enable transformative military designs"--
Download or read book Comparison of Different Feature based and Intensity Signature based Matching Algorithms for Automatic Target Recognition in Synthetic Aperture Radar written by Matt Martino and published by . This book was released on 2007 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: The object of Automatic Target Recognition (ATR) for Synthetic Aperture Radar (SAR) involves comparing extracted target signatures (features) to the statistics of features of all potential targets. Central to this processing paradigm is the search algorithm, which helps assess and optimize the favorable effects of multiple image features on recognition accuracy. The ATR algorithms discussed fall into two categories: feature and intensity-based. The feature-based algorithms create binary images of the edges, corners, gradient and ceiling peaks of the tank. The intensity-based signatures are created using algorithms that extract the tank image, block out the background, normalize the MSTAR (Moving and Stationary Target Acquisition and Recognition) data within the tank region and can be exploited to minimize false classifications. Several scenarios will be explored to determine the effectiveness of using the CLEAN method on the ceiling peak feature extraction method and the validity of using the intensity signatures of the MSTAR tank image.
