Download or read book Data Fusion for Adaptive Distributed Detection written by Ari Naim and published by . This book was released on 1989 with total page 180 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Distributed Detection and Data Fusion written by Pramod K. Varshney and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introductory treatment of the fundamentals of decision-making in a distributed framework. Classical detection theory assumes that complete observations are available at a central processor for decision-making. More recently, many applications have been identified in which observations are processed in a distributed manner and decisions are made at the distributed processors, or processed data (compressed observations) are conveyed to a fusion center that makes the global decision. Conventional detection theory has been extended so that it can deal with such distributed detection problems. A unified treatment of recent advances in this new branch of statistical decision theory is presented. Distributed detection under different formulations and for a variety of detection network topologies is discussed. This material is not available in any other book and has appeared relatively recently in technical journals. The level of presentation is such that the hook can be used as a graduate-level textbook. Numerous examples are presented throughout the book. It is assumed that the reader has been exposed to detection theory. The book will also serve as a useful reference for practicing engineers and researchers. I have actively pursued research on distributed detection and data fusion over the last decade, which ultimately interested me in writing this book. Many individuals have played a key role in the completion of this book.

Download or read book Distributed Data Fusion for Network Centric Operations written by David Hall and published by CRC Press. This book was released on 2017-12-19 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book On Adaptive and Distributed CFAR Detection with Data Fusion written by Stelios Demetrios Himonas and published by . This book was released on 1989 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book An Adaptive Fusion Model for Distributed Detection Systems with Unequiprobable Sources written by Yuzheng Zhang and published by . This book was released on 1994 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a traditional communication system, a single sensor such as a radar or a sonar is used to detect targets. Since the reliability of a single sensor is limited, distributed detection systems in which several sensors are employed simultaneously have received increasing attention in recent years. We consider a distributed detection system which consists of a number of independent local detectors and a fusion center. Chair and Varshney have derived an optimal decision rule for fusing decisions based on. the Baysian criterion. To implement such a rule, the probability of detection PD and the probability of false alarm PF for each local detector must be known. This thesis introduces an adaptive fusion model using the fusion result as a supervisor to estimate the PD and PF The fusion results are classified as "reliable" and "unreliable". Reliable results will be used as a reference to update the weights in the fusion center. Unreliable results will be discarded. The thesis concludes with simulation results which conform to the analysis.

Download or read book Handbook of Multisensor Data Fusion written by Martin Liggins II and published by CRC Press. This book was released on 2017-01-06 with total page 872 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the years since the bestselling first edition, fusion research and applications have adapted to service-oriented architectures and pushed the boundaries of situational modeling in human behavior, expanding into fields such as chemical and biological sensing, crisis management, and intelligent buildings. Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition represents the most current concepts and theory as information fusion expands into the realm of network-centric architectures. It reflects new developments in distributed and detection fusion, situation and impact awareness in complex applications, and human cognitive concepts. With contributions from the world’s leading fusion experts, this second edition expands to 31 chapters covering the fundamental theory and cutting-edge developments that are driving this field. New to the Second Edition— · Applications in electromagnetic systems and chemical and biological sensors · Army command and combat identification techniques · Techniques for automated reasoning · Advances in Kalman filtering · Fusion in a network centric environment · Service-oriented architecture concepts · Intelligent agents for improved decision making · Commercial off-the-shelf (COTS) software tools From basic information to state-of-the-art theories, this second edition continues to be a unique, comprehensive, and up-to-date resource for data fusion systems designers.

Download or read book Distributed Data Fusion for Network Centric Operations written by David Hall and published by CRC Press. This book was released on 2017-12-19 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book New Results in Distributed Detection and Data Fusion for Target Tracking written by Constantino Rago and published by . This book was released on 1995 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimization of Distributed Detection Systems in the Presence of Wireless Channel Uncertainty written by Hamidreza Ahmadi and published by . This book was released on 2013 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: "We study data fusion in a distributed detection system, consisting of several geographically dispersed signal detectors and a fusion center (FC), that is tasked with solving an underlying binary hypothesis testing problem (e.g., detection of a signal source or a target in a field being monitored). Each detector makes a binary local decision based on its local observation, where each local decision has a certain reliability index, determined by the observation quality. These local decisions are digitally modulated and transmitted over wireless channels to neighboring detectors and/or the FC. The FC is tasked with fusing the data received from the detectors and making a global binary decision. The challenge in data fusion is that the binary local decisions would be corrupted due to wireless channel effects (i.e., additive Gaussian noise and multipath fading). These effects further limit the reliability of the global decision. This raises a key question: Aiming to maximize the reliability of the global decision, what is the optimal distributed detection system design in the presence of wireless channel uncertainty? To address this question in this thesis, we identify and address three subproblems as the following: P1) Suppose the topology (i.e., the wireless connections between the local detectors and the FC that are used for transmission of local binary decisions) of the distributed detection system is adaptive and can be selected based on the observation and communication channel qualities. What are the best network topology and the best signal processing schemes (i.e., local decision rules and data fusion rules)? How are the best topology and the best signal processing schemes related to the reliability indices of the local decisions, channel noise and fading? Our results indicate that the optimality of widely used parallel topology, in which the local detectors directly communicate with the FC, is limited. We also demonstrate the average performance gain of topology adaptation compared with a fixed topology system. P2) Channel estimation is an integral part of most of today's wireless communication systems. Via transmitting known training symbols, the local detectors enable the FC to estimate the unknown fading channel, which is used for recovering data symbols. Considering a distributed detection system with a parallel topology, in which the local detectors transmit training symbols, followed by their local binary decisions, and assuming an average transmit power constraint, we ask: What is the best data fusion rule at the FC? How is this fusion rule affected by channel estimation error, transmit power allocation between training and data symbols, and the communication reception mode at the FC (i.e., coherent versus noncoherent)? Our study shows that with noncoherent reception, the detection performance of the FC is maximized when no training symbol is transmitted and all transmit power is spent for only data symbols. This performance is attainable with statistics-based likelihood-ratio-test (LRT) rule for random channels and generalized LRT (GLRT) for deterministic channels. With coherent reception, however, the optimal power allocation depends on the fading model. For Rayleigh fading model, the total detection probability and error exponent are maximized when half of the transmit power is spent for training symbols. Whereas, for Rician fading model, the optimal power allocation depends on the operating signal-to-noise (SNR) and Rice factor. P3) Suppose the distributed detection system is tasked with detecting a Gaussian signal source, where in its presence, local observations are statistically correlated samples of the signal source, corrupted by an additive Gaussian noise. We ask: What is the best linear data fusion rule at the FC? How is this fusion rule affected by the statistical correlation, the reliability indices of the local decisions, transmit power constraints at the local detectors, communication multiple access scheme (employed by the local detectors to communicate with the FC), the communication reception mode at the FC, channel noise and fading? We show that statistical correlation degrades the detection probability of the system. We also find the optimal power allocation for different communication multiple access schemes, subject to several transmit power constraints, in terms of observation and wireless channel qualities"--Pages v-vi.

Download or read book Adaptive Distributed Detection with Applications to Cellular CDMA written by Jian-Guo Chen and published by . This book was released on 1997 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chair and Varshney have derived an optimal rule for fusing decisions based on the Bayesian criterion. To implement the rule, probabilities of detection PD and false alarm PF for each detector must be known, which is not readily available in practice. This dissertation presents an adaptive fusion model which estimates the PD and PF adaptively by a simple counting process. Since reference signals are not given, the decision of a local detector is arbitrated by the fused decision of all the other local detectors. Adaptive algorithms for both equal probable and unequal probable sources, for independent and correlated observations are developed and analyzed, respectively. The convergence and error analysis of the system are analytically proven and demonstrated by simulations. In addition, in this dissertation, the performance of four practical fusion rules in both independent and correlated Gaussian noise is analyzed, and compared in terms of their Receiver Operating Characteristics (ROCs). Various factors that affect the fusion performance are considered in the analysis. By varying the local decision thresholds, the Rocs under the influence of the number of sensors, signal-to-noise ratio (SNR), the deviation of local decision probabilities, and correlation coefficient, are computed and plotted, respectively. Several interesting and key observations on the performance of fusion rules are drawn from the analysis. As an application of the above theory, a decentralized or distributed scheme in which each fusion center is connected with three widely spaced base stations is proposed for digital cellular code-division multi-access communications. Detected results at each base station are transmitted to the fusion center where the final decision is made by optimal fusion. The theoretical analysis shows that this novel structure can achieve an error probability at the fusion center which is always less than or equal to the minimum of the three respective base station. The performance comparison for binary coherent signaling in Rayleigh fading and log-normal shadowing demonstrates that the decentralized detection has a significant increased system capacity over conventional macro selection diversity. This dissertation analyzes the performance of the adaptive fusion method for macroscopic diversity combination in the wireless cellular environment when the error probability information from each base station detection is not available. The performance analysis includes the derivation of the minimum achievable error probability. An alternative realization with lower complexity of the optimal fusion scheme by using selection diversity is also proposed. The selection of the information bit in this realization is obtained either from the most reliable base station or through the majority rule from the participating base stations.

Download or read book Distributed Detection Using a Data Fusion Center with 2 bit Memory written by Huang-Hsiao Kao and published by . This book was released on 1995 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Information Fusion in Distributed Sensor Networks with Byzantines written by Andrea Abrardo and published by Springer Nature. This book was released on 2020-07-14 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the most powerful attack strategies and potential defense mechanisms, always approaching the interplay between the Fusion Center and the Byzantines from a game-theoretic perspective. For each of the settings considered, the equilibria of the game and the corresponding payoffs are derived, shedding new light on the achievable performance level and the impact that the presence of the Byzantines has on the accuracy of decisions made by the Fusion Center. Accordingly, the book offers a simple yet effective introduction to the emerging field of adversarial information fusion, providing a wealth of intuitive take-home lessons for practitioners interested in applying the most basic notions to the design of practical systems, while at the same time introducing researchers and other readers to the mathematical details behind the theory.

Download or read book A Theory for Distributed Signal Detection and Data Fusion written by and published by . This book was released on 2000 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research attempts to develop a fundamental understanding of the issues involved in the design and performance analysis of distributed detection schemes. Such knowledge is currently lacking. This is especially true for cases with statistically dependent observations from sensor to sensor, a practical case on which this research focuses. Some emphasis is being devoted to developing design algorithms and on applications. The goal of these studies is to produce tools and techniques for pressing practical problems. We classify our efforts into four basic areas: properties of dependent observations cases, design algorithms, applications and image fusion.

Download or read book Application aware In network Service and Data Fusion Frameworks for Distributed Adaptive Sensing Systems written by Pan Ho Lee and published by . This book was released on 2009 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Fundamentals of Multisite Radar Systems written by V S Chernyak and published by Routledge. This book was released on 2018-05-02 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an original and comprehensive monograph on the increasingly important field of Multistatic Radar Systems. The material covered includes target detection, coordinate and trajectory parameter estimation, optimum and suboptimum detectors and external interferences. The practical problems faced by those working with radar systems are considered - most algorithms are presented in a form allowing direct use in engineering practice, and many of the results can be immediately applied to information systems containing different types of sensors, not only radars. This book is the revised international edition of Chernyak's renowned Russian textbook.

Download or read book Masters Theses in the Pure and Applied Sciences written by Wade H. Shafer and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Masters Theses in the Pure and Applied Sciences was first conceived, published, and disseminated by the Center for Information and Numerical Data Analysis and Synthesis (CINDAS) * at Purdue University in 1957, starting its coverage of theses with the academic year 1955. Beginning with Volume 13, the printing and dissemination phases of the activity were transferred to University Microfilms/Xerox of Ann Arbor, Michigan, with the thought that such an arrangement would be more beneficial to the academic and general scientific and technical community. After five years of this joint undertaking we had concluded that it was in the interest of all con cerned if the printing and distribution of the volumes were handled by an interna tional publishing house to assure improved service and broader dissemination. Hence, starting with Volume 18, Masters Theses in the Pure and Applied Sciences has been disseminated on a worldwide basis by Plenum Publishing Cor poration of New York, and in the same year the coverage was broadened to include Canadian universities. All back issues can also be ordered from Plenum. We have reported in Volume 34 (thesis year 1989) a total of 13,377 theses titles from 26 Canadian and 184 United States universities. We are sure that this broader base for these titles reported will greatly enhance the value of this important annual reference work. While Volume 34 reports theses submitted in 1989, on occasion, certain univer sities do report theses submitted in previous years but not reported at the time.

Download or read book Distributed Detection Theory and Data Fusion written by and published by . This book was released on 1994 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sampling and quantization problems for distributed detection problems are investigated. Sampling algorithms for weak signal detection problems are derived. Sampling and quantization issues under communication constraints are treated. A new paradigm for distributed detection is presented.