Each of these sensors has advantages and disadvantages. In this paper, we present a framework for sensor data fusion and then postulate a set of principles based on experiences from building systems. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. This textbook provides a comprehensive introduction to the concepts and idea of. These methods and algorithms are presented using three different. No previous knowledge of multisensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended. Typically, this insight is either unobtainable otherwise or a fusion result. Advances in sensor fusion for remote emotive computing emotion sensing and processing could also lead to exciting new applications in the future, including smart healthcare. Sensor fusion is also known as multisensor data fusion and is a subset of information fusion. This book provides an introduction sensor data fusion, as an information technology as well as a branch of engineering science and informatics. Multisensor data fusion guide books acm digital library. Data fusion is a wide ranging subject and many terminologies have been used interchangeably. This book introduces multisensor fusion, which has emerged as the method of choice for implementing robust systems that can handle imperfect inputs. Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place.
A tool for information assessment and decision making, second edition spie press monograph pm222 lawrence a. Sensor data fusion is the process of combining incomplete and imperfect pieces of mutually complementary sensor information in such a way that a better understanding of an underlying realworld phenom. Implementation approaches for situation and threat assessment. Sensor fusion helps in building a more accurate world model in order for the robot to navigate and behave more successfully. It is an extensively revised second edition of the authors successful book. The main benefit of using scenario generation and sensor simulation over sensor recording is the ability to create rare and potentially dangerous events and test the vehicle algorithms with them. This textbook provides a comprehensive introduction to the concepts and idea of multisensor data fusion. A multisensor data fusion algorithm is developed in this paper, which combines a decentralized estimation approach together with image processing to obtain target attribute information. It represents the first broad, practical text on the subject covering all the technologies and methods associated with multisensor fusion, including. The purpose of this book is to provide an introduction to the theories and techniques of 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. From algorithms and architectural design to applications is a robust collection of modern multisensor data fusion methodologies. Multi sensor data fusion with matlab, written for scientists and researchers, this book explores the three levels of multi sensor data fusion msdf. This book illustrates the benefits of sensor fusion by considering the characteristics of infrared, microwave. Request pdf multisensor data fusionan introduction this textbook.
This book describes the benefits of sensor fusion as illustrated by considering the characteristics of infrared, microwave, and millimeterwave sensors, including the influence of the atmosphere on their performance, sensor system application scenarios that may limit sensor size but still require high resolution data, and the attributes of data fusion architectures and algorithms. It develops a new matlab graphical user interface for evaluating fuzzy implication functions, before using fuzzy logic to estimate the unknown states of a dynamic system by processing sensor data. A really cool branch of mlsignal processing is sensor fusion, sometimes also referred to as data fusion, target tracking, filtering etc. This book comes up with estimates or decisions based on multiple data sources as opposed to more narrowly defined estimates or decisions based on single data sources. Sensor and data fusion for intelligent transportation systems. This book is a unique, comprehensive, and uptodate resource for sensor fusion systems designers. The data fusion model maintained by the jdl joint directors of laboratories data fusion group is the most widelyused method for categorizing data. Sensor and data fusion for intelligent transportation systems introduces readers to the roles of the data fusion processes defined by the joint directors of laboratories jdl data fusion model and the data fusion information group dfig enhancements, data fusion algorithms, and noteworthy applications of data fusion to intelligent transportation systems its. Methodological framework and selected applications mathematical engineering 9783662520161. Sensor fusion using synthetic radar and vision data matlab. This example shows how to generate a scenario, simulate sensor detections, and use sensor fusion to track simulated vehicles. Despite the wide variety of all those application domains the data fusion funct ional model.
Software repositories for lowlevel signal processing functions, as described in the signal processing book, and a highlevel object oriented matlab toolbox for signal and systems, used to produce the examples and figures in the sensor fusion book. Sensor and data fusion concepts and applications book, 1993. Chapter 10 discusses implementation of fusion systems. And as the world is awash with data obtained from numerous and varied processes, there is a need for appropriate statistical methods. Best book for learning sensor fusion, specifically regarding. This book is appropriate for use as an upper division undergraduate or graduate level text book. The image fusion can be categorised differently depending on the type of source data to be fused or on type of image sensors employed and according to the fusion purpose. Principles and techniques for sensor data fusion 1. A new chapter about data fusion issues associated with multipleradar tracking systems has also been added. We introduce the concept of iot and present in detail ten different parameters that govern our sensor data fusion evaluation framework. The camera is a very good tool for detecting roads, reading signs or recognizing a vehicle. Best book for learning sensor fusion, specifically regarding imu and gps integration closed. The revised jdl model sensor data fusion systems can be met in several applications, from military to civilian. Chapters 11 and 12 describe emerging applications and information management.
Using matlab examples wherever possible, multisensor data fusion with matlab explores the three levels of multisensor data fusion msdf. Annotation this book describes the benefits of sensor fusion as illustrated by considering the characteristics of infrared, microwave, and millimeterwave sensors, including the influence of the atmosphere on their performance, sensor system application scenarios that may limit sensor size but still require high resolution data, and the attributes of data fusion architectures and algorithms. Part i presents a coherent methodological framework, thus providing the prerequisites for discussing selected applications in part ii of the book. Information about the android sensor fusion app, and software repositories for the app. Review of mathematical techniques in multisensor data fusion. Varshney, multiobjective evolutionary algorithms for wireless sensor network design, multiobjective optimization in computational intelligence. No previous knowledge of multi sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended. Despite the wide variety of all those application domains the data fusion funct ional model is common and it was developed in 1985 by the u. The aim of sensor fusion is to use the advantages of each to precisely understand its environment.
This book provides an introduction sensor data fusion, as an information. An introduction which was originally published by springerverlag in 2007. Lee h, park k, lee b, choi j and elmasri r issues in data fusion for healthcare monitoring proceedings of the 1st international conference on pervasive technologies related to assistive environments, 18. The emerging technology of multisensor data fusion has a wide range of applications, both in department of defense dod areas and in the civilian arena. Multisensor data fusion with matlab crc press book.
The book instills a deeper understanding of the basics of multisensor data fusion as well as a practical knowledge of. This textbook provides a comprehensive introduction to the theories and techniques of multisensor data fusion. The information in the second edition of this volume has been substantially expanded and updated to incorporate recent approaches to sensor and data fusion, as well as additional application examples. Chapter 8 targets the control of sensor and information resources at level four. Jun 20, 2001 the emerging technology of multisensor data fusion has a wide range of applications, both in department of defense dod areas and in the civilian arena. It is the fusion of the single type of modality taken at the same time but at different conditions and from different angles. Lowlevel data fusion combines several sources of raw data to produce new raw data. Sensor fusion enables context awareness, which has huge potential for the internet of things iot. 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. This paper summarizes the state of the data fusion field and describes the most relevant studies. It represents the first broad, practical text on the subject covering all the technologies and methods associated with multi sensor fusion, including. The integration of data and knowledge from several sources is known as data fusion.
The role of sensor fusion in the internet of things mouser. The expectation is that fused data is more informative and synthetic than the original inputs. Multi sensor data fusion by edward waltz and james llinas, artech house radar library, isbn. It should also be of interest to researchers, who need to process and interpret the sensor data in most scientific and engineering fields. The book contains 100 equations, 75 illustrations and key references. This chapter has summarized the stateoftheart in sensor data fusion for automotive applications, showing that this is a relatively new discipline in the automotive research area, compared to. Multi sensor 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. Internet of things iot has gained substantial attention recently and play a significant role in smart city application deployments. The book then employs principal component analysis, spatial frequency, and waveletbased image fusion algorithms for the fusion of image data from. This book introduces multi sensor fusion, which has emerged as the method of choice for implementing robust systems that can handle imperfect inputs.
As in the first edition, the book discusses the benefits of sensor fusion that accrue when sensors that operate with different phenomenologies or surveil separate volumes of space are used to gather signatures and data about objects or events in their field of view. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input. Joint directors of l aboratories jdl data fusion group. Sensor and data fusionedited by nada milisavljevic.
Sensor fusion is also known as multi sensor data fusion and is a subset of information fusion. Krinkin k and filatov a mobile robot pose estimation based on positionvelocity sensor fusion proceedings of the 24th. Sensor data fusion is the process of combining incomplete and imperfect pieces of mutually complementary sensor information in such a way that a better understanding of an underlying realworld phenomenon is achieved. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources.
Military concepts of situation and threat assessment. A new chapter about data fusion issues associated with. Sensor and data fusion concepts and applications guide books. Multisensor data fusion with matlab, written for scientists and researchers, this book explores the three levels of multisensor data fusion msdf. Now, from personal experience it appears that not that many. Featuring contributions from the worlds leading data fusion researchers and academicians, this authoritative book. Introduction the problem of combining observations into a coherent description of the world is basic to perception. Presents stateoftheart advances in the design of multisensor data fusion algorithms, addressing issues related to the nature, location, and computational ability of the sensors. Data fusion is a research area that is growing rapidly due to the fact that it provides means for combining pieces of information coming from different sourcessensors, resulting in ameliorated overall system performance improved decision making, increased detection capabilities, diminished number of false alarms, improved reliability in various situations at hand with respect to separate. Multisensor data fusionan introduction request pdf.
Tracking and sensor data fusion methodological framework. This book is illustrated with many applications and contains a list of modern. Dec 22, 2016 best book for learning sensor fusion, specifically regarding imu and gps integration closed. Sensor fusion is the process of merging data from multiple sensors such that to reduce the amount of uncertainty that may be involved in a robot navigation motion or task performing. We first enumerate and explain different classification schemes for data fusion. These methods and algorithms are presented using three different categories. 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.
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