Download or read book Automatic Target Recognition Using High Range Resolution Data written by and published by . This book was released on 1998 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new algorithm is presented for Automatic Target Recognition (ATR) using High Range Resolution (HRR) profiles as opposed to traditional Synthetic Aperture Radar (SAR) images. ATR performance using SAR images degrades considerably in case of moving targets due to blurring caused in the cross-range domain. ATR based on HRR profiles, which are formed without Fourier transform in the cross-range, is expected to have superior performance for moving targets with the proposed method. One of the major contributions of this project so far has been the utilization of Eigen-templates as ATR features that are obtained via Singular Value Decomposition (SVD) of HRR profiles. SVD analysis of a large class of HRR data revealed that the Range-space eigenvectors corresponding to the largest singular value accounted for more than 90% of target energy. Hence, it has been proposed that the Range-space Eigen-vectors be used as templates for classification. The effectiveness of data normalization and Gaussianization of profile data in improving classification performance is also studied. With extensive simulation studies it is shown that the proposed Eigen-template based ATR approach provides consistent superior performance with recognition rate reaching 99.5% for the four class XPATCH database. This research project is being conducted in direct collaboration with the Sensors Directorate's ATR Assessment Branch, Wright Laboratories, Wright-Patt AFB, Dayton, Ohio, where it is being monitored by Dr. Rob Williams. A primary objective df this collaborative effort is to complement and augment various other ongoing research activities being conducted or supported by the Wright Labs ATR research team.

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 Using High Range Resolution Profiles written by Vijay Bhatnagar and published by . This book was released on 1998 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automatic Target Recognition Using High Range Resolution Profiles written by Rajesh Vashist and published by . This book was released on 2000 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automatic Target Recognition Using High Resolution Radar Range profiles written by Steven P. Jacobs and published by . This book was released on 1997 with total page 290 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 Probabilistic SVM for Open Set Automatic Target Recognition on High Range Resolution Radar Data written by Jason Daniel Roos and published by . This book was released on 2016 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of Automatic Target Recognition (ATR) on High Range Resolution (HRR) radar data in a scenario that contains unknown targets is of great interest for military and civilian applications. HRR radar data provides greater resolution of a target as well as the ability to perform ATR on a moving target, which gives it an advantage over other imaging systems. With the added resolution of HRR comes the disadvantage that a change in the aspect angle or orientation results in greater changes in the collected data, making classical ATR more difficult. Closed set ATR on HRR radar data is defined when all potential targets are assumed to be part of the training target data base. Closed set ATR has been able to achieve higher rates of correct classification by the selection of proper feature extraction algorithms, however, only a few methods for performing open set ATR have been developed. Open set ATR is the ability to identify and discard when a target is not one of the trained targets. By identifying these untrained targets, the number of misclassified targets is reduced, thereby, increasing the probability of a correct classification in a realistic setting. While the open set ATR produces a more realistic approach, the classical closed-set ATR is the standard method of ATR. One of the more popular classification algorithms currently used today is the Support Vector Machine (SVM). The SVM by nature only works on a binary closed-set problem. However, by extracting probabilities from an SVM as proposed by Platt [1], this classification algorithm can be applied to open set. In this thesis, the feature extraction methods established in closed-set ATR are modified to facilitate the application of the Probabilistic Open Set Support Vector Machine (POS-SVM). Utilizing the Eigen Template (ET) and Mean Template (MT) feature extraction methods developed for closed-set ATR, in combination with centroid alignment, an open set ATR Probability of correct classification (PCC) rate of 80% has been achieved. Utilizing POS-SVM, it is possible to successfully perform open set ATR on HRR data with a high PCC.

Download or read book Feature Study for High range resolution Based Automatic Target Recognition written by Junshui Ma and published by . This book was released on 2001 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automatic Target Recognition written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-04 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Automatic Target Recognition written by and published by . This book was released on 2007 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Performance Analysis of Automatic Target Recognition Using High Resolution Radar written by Vikas S. Kedia and published by . This book was released on 1998 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Physics of Automatic Target Recognition written by Firooz Sadjadi and published by Springer Science & Business Media. This book was released on 2007-09-04 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Analysis of High Resolution Polarimetry Data of Static Targets in Automatic Target Recognition Context written by Nicholas M. Sandirasegaram and published by . This book was released on 2007 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Pattern Recognition for Synthetic Aperture Radar SAR Automatic Target Recognition ATR Volume 2 written by and published by . This book was released on 2001 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art research on spectral estimation, feature extraction, and pattern recognition algorithms are presented for radar signal processing and automatic target recognition. Advanced space-time spectral estimation algorithms are presented for multiple moving target feature extraction as well as clutter and jamming suppression for airborne high range resolution (HRR) phased-array radar. A nonparametric adaptive filtering-based approach, referred to as the Gapped-data Amplitude and Phase EStimation (GAPES) algorithm, is proposed for the spectral analysis of gapped data sequences as well as synthetic aperture radar (SAR) imaging with angle diversity data fusion. A QUasi-parametric ALgorithm for target feature Extraction (QUALE) algorithm is also investigated for angle diversity data fusion. Support Vector Machines (SVMs) as compared with other advanced classifiers in the MSTAR Public Domain Release and HRR data are found to outperform neural networks and matched filters. A new concept to create negative examples from the known target class is presented and shown to tremendously improve the rejection of confusers. Finally, Information Theoretic Learning (ITL) is proposed as a new algorithm to demix HRR signatures of closely parked targets.

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 Target Recognition Using Linear Classification of High Range Resolution Radar Profiles written by Ricardo A. Diaz and published by . This book was released on 2004-03-01 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: High Range Resolution (HRR) radar profiles map three-dimensional target characteristics onto one-dimensional signals that represent reflected radar intensity along target extent. In this thesis, second through fourth statistical moments are extracted from HRR profiles and input to Fisher Linear Discriminant (FLD) classifiers. An iterative classification process is applied that gradually minimizes required a priori knowledge about the target data. It is found that the second through fourth statistical moments of HRR profiles are useful features in the FLD classification of dissimilar targets and they provide reasonable discrimination of similar targets. Greater than 69% correct classification for two-target scenarios and greater than 60% correct classification for three-target scenarios is obtained using a single HRR profile extracted from a full 360-degree aspect angle window. A key contribution of this thesis is the demonstration that simple statistical moment features and simple linear classifiers can be used to effectively classify HRR profiles.

Download or read book Advanced Automatic Target Recognition written by Ram Chellappa and published by . This book was released on 1998 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: This final report summarizes the findings of the research, Advanced Automatic Target Recognition, supported by AFOSR grant P4962O-97-I-O523. In this research effort, we have developed a method for detecting buildings from SAR images, so that false alarms due to building returns can be reduced. We consider three scenarios corresponding to incremental data availability from a high-resolution, airborne SAR, multiple SAR images and interferometric SAR. In a single strip-map SAR image, we look for certain characteristics exhibited by buildings in radar imagery, namely the combination of cardinal streaks and supporting shadow, to delineate buildings. We then present a framework for registering multi-pass airborne SAR images and for extracting heights of 3-D structures which produce identifiable linear patterns in them. Finally, given noisy elevation data derived from an interferometric (IF) SAR, buildings are segmented from the ground using a local histogram-based thresholding scheme, consolidated by propagating the thresholds, and refining along their edges to reduce errors. The effectiveness of the building detection and height estimation algorithms is demonstrated using examples of high-resolution SAR data from Lincoln Laboratory's ADTS radar and elevation data derived from Sandia's IPSAR platform. Our results will make possible on-the-fly, context-based exploitation of SAR images.