The theories of uncertainty make it possible to benefit from such information, but the implementation of these theories requires specific developments to meet the needs of multisensor data fusion. In contrast to the probability and evidence theories, which are. Uncertainty theories and multisensor data fusion iste kindle edition by appriou, alain. Although multisensor data fusion is still not regarded as a formal professional discipline, tremendous progress has been made since the publication of the first edition of this book in 1992. Moyan xiao and zhibiao he multisensor data fusion based on the second generation curvelet transform. Each sensor, is supposed to locally track a set of randomly appearing and disappearing targets using interacting multiple models imm algorithms 1, 2.
The latest edition covers these areas as well as smart agents, human computer interaction, cognitive aides to analysis and data system fusion control. What is the purpose and the specificity of information fusion processing in multiple sensor systems. The epub format of this title may not be compatible for use on all handheld devices. Decisionmaking algorithm, as the key technology for uncertain data fusion, is the core to obtain reasonable multisensor information fusion results. Implementation of multisensor data fusion algorithm. This paper aims to describe an improved method for integrating the sar features into multispectralms images based on the second generation curvelet transform. Sensor grids, flexible communications networks and webbased services provide for.
The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of the problems that can be faced during its execution. Mathematical techniques in multisensor data fusion david. Rule sets for higherlevel afe in a cots environment. Ds evidence theory is a typical and widely applicable decisionmaking method. Mathematics of data fusion theory and decision library b.
These terminologies and ad hoc methods in a variety of scientific, engineering, management, and many other publications, shows the fact that the same concept has been studied repeatedly. Data integration is a large part of the multisensor data fusion process, however, and might be considered a building block for building more advanced data sets. Handbook of multisensor data fusion edited by david hall and james llinas crc press, 2001. From algorithms and architectural design to applications covers the contemporary theory and practice of multisensor data fusion, from fundamental concepts to cuttingedge techniques drawn from a broad array of disciplines. School of computer science and information engineering.
For example, human brain uses data fusion by combining sight, smell, touch, hearing, and taste to make an inference regarding the surrounding environment. Fusion of multisensor data collected by military robots. Multisource data vary in spectral, spatial and temporal resolutions necessitating advanced analytical or numerical techniques for enhanced interpretation capabilities. The combination of these specific sets of data is a defining feature of multisensor data fusion and differentiates information fusion from data integration. However, ds evidence theory makes decisions without considering the sensors difference, which may lead to illogical results. From algorithm and architecture design to applications 1. Featuring contributions from the worlds leading data fusion researchers and academicians, this authoritative book.
Catalog start subject multisensor data fusion remove constraint subject. In recent years, benefits of multisensor fusion have motivated research in a variety of application areas as follows. Importantly, different fusion methods are evaluated in this paper. Springerverlag, 1997, barshaloms book multitargetmultisensor tracking.
Multisensor data fusion based on the second generation. Dempstershafer ds theory is an approach to assign beliefs to evidence with combination rule. Multisensor data fusion technology using a neural network. Multisensor data fusion technology based on dempstershafer evidence theory is widely applied in many fields. Uncertainty theories and multisensor data fusion alain. Data fusion is an advanced technique to combine information coming from several sources in order to obtain more accurate results. Data fusion or information fusion are names which have been primarily assigned to militaryoriented problems. Pdf handling data uncertainty and inconsistency using.
Advanced photonics journal of applied remote sensing. Multisensor data and knowledge fusiona proposal for a. Bayesian decision rules are optimal for any pdf of the data gaussian or nongaussian, any prior pdf. Multisensor data fusion seeks to combine data from multiple sensors to perform inferences that. Research article optimal fusion filtering in multisensor stochastic systems with missing measurements and correlated noises r. Exploitation of the diversity of information sources. For instance, a sensor may record many different sets of temperatures within a certain period of time and later build a larger set over a longer period of time.
Flyeruncertainty theories and multisensor data fusion. Uncertainty theories and multisensor data fusion appriou. Johns college august 31, 1999 signal processing and communications laboratory department of engineering university of cambridge. Handbook of multisensor data fusion theory and practice second edition edited by martin e.
Download it once and read it on your kindle device, pc, phones or tablets. Addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first discusses basic questions such as. In this section, we discuss data fusion from a theoretical perspective. This mathematical definition is a theory of evidence because it. Since the publication of the first edition of this book, advances in algorithms, logic and software tools have transformed the field of data fusion. It is aimed at advanced undergraduate and firstyear graduate students in. Decisionmaking algorithm for multisensor fusion based on. The basic concept of decisionlayer multisensor data fusion, i. Fusing data in uncertainty conditions requires two main steps. Multiple sensor fusion has been a topic of research since long. Preface xiii chapter 1 introduction to multisensor data fusion 1 1. An integrated model for robust multisensor data fusion.
His main personal scientific contribution concerns on the one hand data fusion and signal processing for sensor systems, and on the other hand theoretical developments for uncertainty theories. Uncertainty theories and multisensor data fusion wiley online. Multisensor data fusion multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. The problem of multisensor raster and vector data fusion is formulated as follows. This article discusses the elementary theory and method of condition monitoring and fault diagnosis in complex mechanical systems, using multisensor data fusion and integration technology. Each sensor, is supposed to locally track a set of randomly appearing and. However, distributed multisensor data fusion is not without technical challenges. The focus of this paper is on multisensor data fusion. Emerging applications of multisensor fusion and integration redundant and complementary sensor data can be fused and integrated using multisensor fusion techniques to enhance system capability and reliability.
Pdf uncertainty theories and multisensor data fusion. Multisensor data fusion is an emerging technology applied to department of defense dod areas such as automated target recognition, battlefield surveillance, and guidance and control of. In this paper, we present a framework for sensor data fusion and then. The catholic university of korea, republic of korea. Addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first discusses basic. With this second edition, the authors have been successful in updating us with stateoftheart methods and techniques in multisensor data fusion.
Data fusion is a wide ranging subject and many terminologies have been used interchangeably. Mar 29, 2005 although multisensor data fusion is still not regarded as a formal professional discipline, tremendous progress has been made since the publication of the first edition of this book in 1992. Conference proceedings papers presentations journals. Uncertainty theories and multisensor data fusion alain appriou, onera, france isbn. Uncertainty theories and multisensor data fusion wiley. Mcmullen and a great selection of similar new, used and collectible books available now at great prices. Multisensor data fusion is a key enabling technology in which information from a number of sources is integrated to form a unified picture 1.
This should include, the wiley titles, and the specific portion of the content you wish to reuse e. Review of mathematical techniques in multisensor data. Vershinin school of engineering, coventry university, coventry, cv1 5fb, u. Research article optimal fusion filtering in multisensor.
From algorithms and architectural design to applications is a robust collection of modern multisensor data fusion methodologies. Chapter 1 introduction to multisensor data fusion 1 1. Image fusion is a formal framework which is expressed as means and tools for the alliance of multisensor, multitemporal, and multiresolution data. This model proposes an alternative to bayesian theory. In the years since the bestselling first edition, fusion research and applications have adapted to serviceoriented 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. Multiple sensor fusion for detec tion, classification and tracking of. Download citation uncertainty theories and multisensor data fusion addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first. This textbook provides a comprehensive introduction to the theories and techniques of multisensor data fusion.
Uncertainty theories and multisensor data fusion iste. Many theories have been applied in multisensor data fusion, such as the bayesian approach, evidential theory, fuzzy set theory, and rough set theory. However, how to determine basic belief assignment bba is still an open issue. Perception, aaai workshop on spatial reasoning and multisensor fusion, kaufmann. From algorithm and architecture design to applications 20. The experimental results show that the design of tcm based on the feature level fusion can significantly improve the accuracy of the tool condition classification. The existing bba methods pay more attention to the uncertainty of information, but do not simultaneously consider. An efficient algorithm for multisensory data fusion under uncertainty. Dempstershafer evidence theory plays a crucial role. How can the available measurements be characterized in such a case. The main objective of multisensor data fusion is to combine elements of raw data from multiple sensors to extract the greatest amount of information possible about the sensed environment which is greater than the sum of its contributing parts. Moyan xiao and zhibiao he multisensor data fusion based on the second generation curvelet transform, proc. Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book. Sensor fusion multisensor data fusion felix riegler 8.
Uncertainty theories and multisensor data fusion by alain. In military applications, typical data fusion problems are. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization. It reflects new developments in distributed and detection fusion, situation and impact awareness in complex applications, and human cognitive.
Hero iii and sridhar lakshmanan multisensor data fusion technology combines data and information from multiple sensors to achieve improved accuracies and better inference about the environment than could be achieved by the use of a single sensor. Theory and practice, second edition represents the most current concepts and theory as information fusion expands into the realm of networkcentric architectures. Bayesian approaches to multisensor data fusion a dissertation submitted to the university of cambridge for the degree of master of philosophy olena punska, st. Full text of handbook of multisensor data fusion see other formats. Distributed decision or estimation fusion prob lems for cases with statistically independent observations or observation noises have received significant attention see varshneys book distributed detec tion and data fusion, new york.
Reasoning and objectoriented data processing for multisensor data fusion dvanced information technologies provide indispensable contribution to peacekeeping and other crisis response operations. General data fusion methods stereo vision conclusion starr and desforges 1998 data fusion is a process that combines data and knowledge from di erent sources with the aim of maximising the useful information content, for improved reliability or discriminant capability, while minimising the quantity of data ultimately retained. Multisensor data fusion technology data fusion is the process of combining data or information to estimate or predict entity states steinberg and bowman 2001 deduction action computer running a data fusion algorithm. Mathematical techniques in multisensor data fusion artech house information warfare library 9781580533355 by david l. Karray pattern analysis and machine intelligence lab, university of waterloo, waterloo, on, canada. An improved multisensor data fusion method and its. Use features like bookmarks, note taking and highlighting while reading uncertainty theories and multisensor data fusion iste. On the other hand, recent investigations on the actual spectrum utilization have shown that a portion. Multisensor data fusion is the process of combining observations from a number of different sensors to provide a robust and complete description of an environment or process of interest. Uncertainty degree of bpa that can be presented as follows. In ds theory, each source can have a different level of detail durrantwhyte and henderson, 2008. Parametric and nonparametric approaches for multisensor data fusion by bing ma cochairs. Presents stateoftheart advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors describes new materials and achievements in.
Review of mathematical techniques in multisensor data fusion. Multisensor decision and estimation fusion yunmin zhu. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. Pdf data provided by sensors is always subjected to some level of uncertainty. The dempster shafer evidence theory dset, also known as the evidential theory, is a flexible method in multisensor data fusion 911.
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