Introduction to sensor data fusion methods and applications last lecture. What makes professional series detectors different is the way this data is used. This paper explained the concepts, methods and applications of image fusion as a contribution to multisensor integration oriented data processing. Use features like bookmarks, note taking and highlighting while reading multisensor data fusion. 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. Introduction multisensor data fusion is a technology to enable combining. It is aimed at advanced undergraduate and firstyear graduate students in electrical engineering and computer science, as well as researchers and professional engineers.
Multisensor data fusion is a technology to enable combining information from several sources in order to form a unified picture. Motivation, general context discussion of examples oral examination. Introduction the problem of combining observations into a coherent description of the world is basic to perception. An introduction to multisensor data fusion david l. The book is intended to be largely selfcontained in so far as the subject of multisensor data fusion is concerned, although some prior exposure to the subject may be helpful to the reader. In my next set of blog posts, i will explore the world of multisensor data fusion. With the development of advanced manufacturing technology, many complex surfaces such as freeform surfaces and structured.
Applications of data fusion cross a wide spectrum, including environment monitoring, au. Pdf an introduction to multisensor data fusion semantic scholar. Study of data fusion algorithms applied to unattended. 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. Mathematical techniques in multisensor data fusion 2nd ed. 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. Principles and techniques for sensor data fusion 1.
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 alone. Vershinin school of engineering, coventry university, coventry, cv1 5fb, u. Chapter 1 introduction to multisensor data fusion 1 1. This course provides an introduction to multisensor information fusion. Techniques for multisensor data fusion are drawn from a wide range of areas. In 1997, hall and llinas gave a general introduction to multisensor data fusion. Multisensor data fusion is an emerging technology applied to department of defense dod areas such as automated target recognition, battlefield surveillance. From algorithm and architecture design to applications 1. The purpose of this book is to provide an introduction to the theories and techniques of multisensor data fusion. With this second edition, the authors have been successful in updating us with stateoftheart methods and techniques in multisensor data fusion. A systembased approach is defined for the application of data fusion systems within engineering. 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 autonomous vehicles, and to nondod applications such as monitoring of complex machinery, medical diagnosis, and smart buildings.
Dirty secrets in multisensory data fusion, a392879. B download it once and read it on your kindle device, pc, phones or tablets. It should also be useful to advanced undergraduates in electrical engineering or. This textbook provides a comprehensive introduction to the theories and techniques of multisensor data fusion. We can consider the input of data fusion as a twodimensional. Each sensor, is supposed to locally track a set of randomly appearing and disappearing targets using interacting multiple models imm algorithms 1, 2. Generic software architecture for development of data. To avoid confusion on the meaning, dasarathy decided to use the term \information fusion as the overall term for fusion of any kind of data 20. Data fusion, maritime surveillance, situation assessment, threat assessment, strategic defense. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the phd. Issues and challenges in situation assessment level 2 fusion pdf. Multisensor data fusionan introduction request pdf. It could be useful to all specialists working in the area of data fusion and c4i systems development.
Teaching sensor fusion and kalman filtering using a. Multisensor data fusion in distributed sensor networks. Data fusion is the process of integrating multiple data sources to produce more consistent. The authors elucidate df strategies, algorithms, and performance evaluation mainly for aerospace applications. Introduction the estimation is the procedure of determining the state of a system from noisy measurements, taking account of measurement errors and system disturbances. Introduction to multisensor data fusion techniques. Preprocessing to demonstrate the feasibility of the poi method, the cad model was taken as a base techion. Review of mathematical techniques in multisensor data. Hall, senior member, ieee, and james llinas invited paper multisensor data fusion is an emerging technology applied to department of defense. In this paper, we present a fuzzybased data fusion approach for wsn with the aim of increasing the qos whilst reducing the energy consumption of the sensor network. Pitfalls in data fusion systems international society of information. He has published widely in the fields of image processing, fuzzy logic and sensor fusion and is the author of a recent textbook on data fusion multi.
Multisensor data fusion 5 information sources, the use of nonprobabilistic methods in describing uncertainty and general decision making processes. What links here related changes upload file special pages permanent link page information wikidata. Review the kalman filtering problem for state estimation and sensor fusion describes extensions to kf. Data fusion systems are now widely used in various areas such as sensor networks, robotics, video and image processing, and intelligent system design, to name a few. The book has been designed as a text for a onesemester graduate course in multisensor data fusion. In the second part, common fusion techniques are presented. Abstract in this paper, data obtained from wireless unattended.
Based on available data, it is assumed that each sensor provides a set of data aligned by the internal preprocessing of the sensor. Using multiple sensors, planners can generate more robust data models or obtain greater numbers of data points for the purposes of a given system. Data fusion is a process that seeks to improve the ability to estimate the position, velocity. The process of automatically filtering, aggregating, and extracting the desired information from multiple sensors and sources, and integrating and interpreting data is an emerging technology, commonly referred to as either sensor, data, or information fusion. With a diverse set of mathematical and heuristic techniques for combining data from multiple sources, the book shows how to implement a data fusion system, describes the process for algorithm selection, functional architectures and requirements for ancillary software, and illustrates manmachine interface requirements an database issues. Data fusion techniques combine data from multiple sensors and related. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas.
B when citing this work, cite the original article. Study of data fusion algorithms applied to unattended ground sensor network pannetier b. An introduction to multisensor data fusion ieee journals. A new multisensor track fusion architecture for multi. Obc08, chapter 4 kalman filtering obc08, chapter 5 sensor fusion hyconeeci, mar 08 r. Multisensor data and knowledge fusiona proposal for a.
Fusing multisensor data enlarges the field of view and increases the certainty and precision of the estimates. Multisensor data fusion for trajectory prediction of a. In this paper, we present a framework for sensor data fusion and then postulate a set of principles based on experiences from building systems. An introduction to bayesian and dempstershafer data fusion executive summary data fusion is a relatively new. This book is an important introduction to multisensor data fusion technology and its application in military command, control, and intelligence operations. Data fusion, fuzzy logic, kalman filter, targettracking. Comments ar e made on the stateoftheart in data fusion. Introduction to sensor data fusion methods and applications. A multisensor data fusion method based on gaussian process. Introduction to multisensor data fusion 3 credits understanding the concepts, techniques, and issues surrounding the fusion of information from multiple sensors and sources of data. In this section, we provide a brief summary of existing approaches to this problem. A crucial part of a fusion system is the data association, which requires data synchronization. The authors elucidate df strategies, algorithms, and performance evaluation mainly for aerospace.
Like most existing technologies, multisensor data fusion is an attempt to take a phenomenon that exists in nature and translate it into a technology usable by manmade systems. Multisensor data fusion mitchell 2007 permits the working status of the process and machinery to be acquired by integrating sensors into manufacturing systems. 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. Luo and kay offered a public data structure based on multi sensor data fusion in 19883. Data base systems with vast context information interaction with humans. Introduction to multisensor data fusion conference paper pdf available in proceedings ieee international symposium on circuits and systems 6. Level 34 data fusion, obviously, builds on level 12 methods. The major synchronization strategies for data fusion are discussed and contrasted with respect to their usability in adas. The data knowledge extraction and modelling by using multisensor data fusion section 3. All the information collected by the sensors is fed into a microprocessor that analyzes the data using a complex algorithm.
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