Data fusion processes are often categorized as low, intermediate, or high, depending on the processing stage at which fusion takes place. The different levels of multisensor fusion can be used to provide information to a system that can be used for a variety of purposes. Handling data uncertainty and inconsistency using multisensor. Sensor data fusion using dsm theory for activity recognition. Multisensor fusion and integration seminar report, ppt, pdf. Many theories have been applied in multisensor data fusion, such as the bayesian approach 5, evidential theory 6, fuzzy set theory 7, and rough set theory 8.
How can the available measurements be characterized in such a case. Uncertainty theories and multisensor data fusion iste appriou, alain on. Hall and llinas 3 conducted an overview of multisensor data fusion technology, jdl fusion process model, military and nonmilitary applications. Among sensor data fusion techniques, bayesian methods 8, 9 and evidence theories such as dempstershafer theory dst 12, 2 are commonly used to handle the degree of uncertainty in fusion processes. Multisensor fusion and integration seminar report, ppt. Estimation theory, digital signal processing and computer science are applied in parallel with artificial. Communications and decision theories are related to epistemology and uncertainty management. Research article handling data uncertainty and inconsistency using multisensor data fusion waleeda.
Data fusion methodologies for multisensor aircraft. Dempster shafer ds theory is an approach to assign beliefs to evidence with combination rule. For instance, when no road sign is present, no information is available to the camerabased system. Data provided by sensors is always subjected to some level of uncertainty and inconsistency. The theory of fusion systems download ebook pdf, epub. Pdf data provided by sensors is always subjected to some level of uncertainty. An integrated model for robust multisensor data fusion mdpi. Construction of a general method for data association 197. A bayesian approach to information fusion for evaluating. Uncertainty theories and multisensor data fusion wiley. The focus of this paper is on multisensor data fusion. Data fusion is a wide ranging subject and many terminologies have been used interchangeably.
This theory has been applied in multisensor data fusion including remotely sensed images with the main objective being to improve. Flyeruncertainty theories and multisensor data fusion. Download it once and read it on your kindle device, pc, phones or tablets. However, if these several sources provide inconsistent data. Data fusion methodologies for multisensor aircraft navigation. Mathematical techniques in multisensor data fusion download. Uncertainty theories and multisensor data fusion iste. Motion state estimation for an autonomous vehicle trailer.
Explore multisensor fusion and integration with free download of seminar report and ppt in pdf and doc format. This site is like a library, use search box in the widget to get ebook that you want. A new engine fault diagnosis method based on multisensor. Multisensor data and knowledge fusiona proposal for a. Abdulhafiz 1 andalaakhamis 2 low and medium voltage division, siemens, cairo, egypt ieee senior member, engineering science department, suez university, suez, egypt correspondence should be addressed to waleed a. Active and dynamic information fusion for multisensor systems with dynamic bayesian networks yongmian zhang and qiang ji abstractmany information fusion applications are often characterized by a high degree of complexity because. Among the existing fusion theories, dempstershafer theory was found to be the best. A reliabilitybased multisensor data fusion with application. Issues of uncertainty analysis in highlevel information. Addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first discusses basic questions such as.
The importance of an explicit model of uncertainty was recognized, but the techniques. Uncertainty theories and multisensor data fusion by alain. This uncertainty corrupts the quality of the information fusion system. Uncertainty theories and multisensor data fusion wiley online. Parametric and nonparametric approaches for multisensor data fusion by bing ma cochairs. 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. Highlevel fusion 4, handbook of multisensor data fusion, 56, brainmind machinery 7, highlevel information fusion management and systems design 8. Uncertainty theories and multisensor data fusion iste kindle edition by appriou, alain. Uncertainty theories and multisensor data fusion alain appriou, onera, france isbn. Simple example of the implementation of the method 203. Gumtypea uncertainty, therefore, may be understood as approximation for a sufficiently large number of observations 10.
Click download or read online button to get the theory of fusion systems book now. Fusion of information given for the input quantities it is the intrinsic purpose of the bayesian approach to uncertainty analysis to develop the joint posterior pdf for the output quantity measurand which is. Furthermore, multisensor data fusion can provide additional. Multisensor data fusion algorithms reduce the uncertainty by combining data from. It is aimed at advanced undergraduate and firstyear graduate students in electrical. 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. This method, works based on a clustering technique followed by an mlp predictor. 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. As a matter of fact, the ontologies are seen as a semantic tool for describing and reasoning about sensor data, objects, relations and general domain theories. The fusion of data or information from multiple sensors or a single sensor over time can takes place at different levels of representation.
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. As a result, many sensor fusion methods have been developed based on probabilistic methods andor statistical. An efficient algorithm for multisensory data fusion under. The main tools to representing uncertainty are probability distributions.
The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision calculation. Uncertaintysensitive heterogeneous information fusion assessing threat with soft, uncertain, and con. Multisensor based mobile robots and applications in industrial, space, navigation, and et al. In this paper, we present a framework for sensor data fusion and then. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources. Uncertainty theories and multisensor data fusion alain. Multisensor data fusion 2, concepts, models, and tools for information fusion 3, high level fusion 4, high level information fusion management and systems design 5, and handbo ok of multisensor data fusion, 67. An efficient algorithm for multisensory data fusion under uncertainty. Many theories have been applied in multisensor data fusion, such as the bayesian approach, evidential theory, fuzzy set theory, and rough set theory. 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 theories and techniques of multisensor data fusion. The majority of fusion operators is based on optimistic assumptions about reliability of sources. Perception, aaai workshop on spatial reasoning and multisensor fusion, kaufmann. Most data fusion methods depends on system behavior, which do not allow to easily deal with unknown systems.
Hall, senior member, ieee, and james llinas invited paper 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. As a generalized probabilistic approach, dst, which considers upper and lower bounds of probability, has some distinct features. Research article handling data uncertainty and inconsistency. The need for hlif uncertainty analysis is important for measures of performance and measures of effectiveness 8.
Multisensor data fusion can effectively address this problem. Addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first discusses basic. Multisensor data fusion has drawn many researchers interests since multisensory data can improve accuracy and robustness of observations while overcoming physical limitations and inaccuracy of single sensor data. Sensor fusion is combining of sensory data or data derived from disparate sources such that the resulting information has less uncertainty than would be possible when these sources were used individually. At a low level of the jdl model, observations received from sensors are sullied wi th uncertainty, due to operational. Pdf uncertainty theories and multisensor data fusion. Firstly, gaussian types of fault models and test models are established by observations of sensors. Full text of handbook of multisensor data fusion see other formats. This paper addresses multisensory data fusion for unknown systems. Pdf handling data uncertainty and inconsistency using. Information fusion resulting from multisource processing, often called multisensor data fusion when sensors are the main sources of information, is a relatively young less than 20 years technology domain. Multisensorbased mobile robots and applications in industrial, space, navigation, and et al. Land use and land cover change prediction with the theory of.
Introduction to multisensor data fusion conference paper pdf available in proceedings ieee international symposium on circuits and systems 6. Many panel sessions at the international conference on information fusion icif have focused on hlif of which we sought to canvass 9 and organize the discussions on hlif. This site is like a library, use search box in the widget to get ebook. Uncertaintysensitive heterogeneous information fusion. Dempstershafer theory for data fusion applications article pdf available june 2011 with 184 reads how we measure reads. Mathematical techniques in multisensor data fusion. Jun 17, 2014 what is the purpose and the specificity of information fusion processing in multiple sensor systems. Active and dynamic information fusion for multisensor. The book has been designed as a text for a onesemester graduate course in multisensor data fusion. Uncertainty theories and multisensor data fusion alain appriou addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first discusses basic questions such as.
Variously called multisensor data fusion, sensor data fusion or. An introduction to multisensor data fusion david l. 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. Possibility theorys treatment of imperfect data is similar in spirit to probability. On context awareness for multisensor data fusion in iot.
In multisensor data fusion, the information to be handled is always random, vague, imprecise and heterogeneous. In general, multisensor data fusion is motivated by 1 reducing overall redundant information from different sensors, 2 increasing information gain due to the utilization of multiple sensors, and 3 increasing the accuracy and decreasing the uncertainty of the system. An introduction to multisensor data fusion ieee xplore. The dempster shafer evidence theory dset, also known as the evidential theory, is a flexible method in multisensor data fusion 911. An integrated model for robust multisensor data fusion.
So, filtering in estimation theory includes much more than just. There are many different data fusion techniques in the literature for multiple sensor systems. The purpose of this book is to provide an introduction to the theories and techniques of multisensor data fusion. The success of information fusion is defined by the quality of knowledge produced by fusion processes, with the latter in turn depending on how well data are represented, how reliable and adequate the model of data uncertainty used, and how accurate and appropriate or applicable prior knowledge is. Historically, the probability theory was used for a long time to deal. Use features like bookmarks, note taking and highlighting while reading uncertainty theories and multisensor data fusion iste.
The developed data fusion framework needs to be able to eliminate the redundancy, uncertainty, and fuzziness of the data sources to achieve robust and accurate fusion results. Click download or read online button to get mathematical techniques in multisensor data fusion book now. It should also be useful to advanced undergraduates in electrical engineering or computer. Land use and land cover change prediction with the theory. The main focus is on identifying and dealing with uncertainty and inconsistent conditions. Issues of uncertainty analysis in highlevel information fusion. Advances in intelligent systems and computing, vol 381. Data fusion is the process of integrating multiple data sources to produce more consistent, accurate, and useful information than that provided by any individual data source. Active and dynamic information fusion for multisensor systems. What is the purpose and the specificity of information fusion processing in multiple sensor systems.
763 610 166 704 1671 1605 189 688 936 237 1460 1073 586 617 89 916 1437 128 232 150 306 286 454 241 830 439 373 1040 454 1021 1114 35 1239 292