Uncertainty theories and multisensor data fusion wiley. Pdf uncertainty theories and multisensor data fusion. What is the purpose and the specificity of information fusion processing in multiple sensor systems. Relative to probability theory 5, ds evidence theory can settle imprecise data and has a more extensive application area. Uncertainty theories and multisensor data fusion appriou. These involve the modeling of qualitative information sources, the use of nonprobabilistic methods in describing uncertainty and general decision making processes. Data provided by sensors is always subjected to some level of uncertainty and inconsistency. Multisensor data fusion in presence of uncertainty and. Research open access dempster shafer fusion of multisensor. The term uncertainty reduction in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging. In this paper, we present a framework for sensor data fusion and then. 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. Multisensor data fusion algorithms reduce the uncertainty by combining data from several sources.
Pdf handling data uncertainty and inconsistency using. The challenge for dimensional metrology lies in making available multiscale measuring devices with suitable concepts for the scaleindependent data fusion. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is significantly lower than the performance of each of the. Furthermore, multisensor data fusion can provide additional. 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. Multi sensor data fusion in presence of uncertainty and inconsistency in data 227 laser proximity sensor. Dempstershafer ds theory is an approach to assign beliefs to evidence with combination rule. However, if these several sources provide inconsistent data, catastrophic fusion may occur where the performance of multisensor data fusion is signicantly lower than the performance of. Multisensor data fusion strategies for advanced driver assistance systems 3 obstacles over a given area, the early detection of a possible collision, possible suggestions for prompt and effective countermeasures e. An efficient algorithm for multisensory data fusion under uncertainty. Decisionmaking algorithm for multisensor fusion based on. Uncertainty theories and multisensor data fusion iste. A reliabilitybased multisensor data fusion with application. 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.
Multisensor data fusion technology based on dempstershafer evidence theory is widely applied in many fields. Theory and practice, second edition represents the most current concepts and theory as information fusion expands into the realm of networkcentric architectures. The main focus is on identifying and dealing with uncertainty and inconsistent conditions. However, how to determine basic belief assignment bba is still an open issue. Uncertainty theories and multisensor data fusion iste kindle edition by appriou, alain. A weighted belief entropybased uncertainty measure for multi.
Peri s part of an officeof naval researchfunded science and technology development task, apl is developing an identificationid sensor data fusion testbed. In this chapter, a new framework of active adas is proposed. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. Ds evidence theory is a typical and w idely applicable decisionmaking method. In this paper, a new fault diagnosis method based on multisensor data fusion is proposed, in which the dempstershafer ds evidence theory is employed to model the uncertainty. Application to gnss positionning and integrity monitoring nourdine ait tmazirte, maan e. Multisensor data fusion based on information theory. We proposed a definition of data fusion in uncertainty condition, data fusion is combining several uncertainty sources to make an effective representation for. Furthermore, accompanied by multi sensor data fusion, how to measure the uncertain degree or the reliability of sensor reports with heterogeneous sources is still an open issue.
Multisensor data fusion can effectively address this problem. 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. Multisensor data fusion in dimensional metrology pdf free. How can the available measurements be characterized in such a case. 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. Multisensor data fusion algorithms reduce the uncertainty by. 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. Decisionmaking algorithm, as the key technology for uncertain data fusion, is the core to obtain reasonable multisensor information fusion results. The need for hlif uncertainty analysis is important for measures of performance and measures of effectiveness 8. Issues of uncertainty analysis in highlevel information fusion.
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. Pdf data provided by sensors is always subjected to some level of uncertainty. Multi sensor data fusion based on information theory. Research article handling data uncertainty and inconsistency. Multisensor data and knowledge fusiona proposal for a. Download citation uncertainty theories and multisensor data fusion addressing recent challenges and developments in this growing field, multisensor data. It reflects new developments in distributed and detection fusion, situation and impact awareness in complex applications, and human cognitive. A new engine fault diagnosis method based on multisensor. Uncertainty theories and multisensor data fusion iste appriou, alain on. Addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first discusses basic. Ds evidence theory, also called dempstershafer theory, is an effective data decisionmaking method to deal with the uncertainty of multisensor information fusion system.
Multisensor data fusion 2, concepts, models, and tools for information fusion 3, highlevel fusion 4, highlevel information fusion management and systems design 5, and handbook of multisensor data fusion, 67. Combining multiple sensors in order to better grasp a tricky, or even critical, situation is an innate human reflex. The testbed is driven by an aplmodifiedversion of the joint composite tracking network pilot. The theory of fusion systems download ebook pdf, epub. In this section, a weighted belief entropy based on deng entropy is proposed. In particular, the definition of data and information fusion differs. Its framework is able to deal with information uncertainty, imprecision, randomness, conflicts and heterogeneity. This paper addresses multisensory data fusion for unknown systems. The importance of an explicit model of uncertainty was recognized, but the techniques.
Pdf multisensor data fusion in presence of uncertainty and. An efficient algorithm for multisensory data fusion under. Fusing data in uncertainty conditions requires two main steps. Uncertainty theories and multisensor data fusion alain appriou, onera, france isbn. Level 34 data fusion problems are considered in less detail. 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.
Perception, aaai workshop on spatial reasoning and multisensor fusion, kaufmann. So, filtering in estimation theory includes much more than just. Data fusion finds wide application in many areas of robotics such as object recognition, environment mapping, and localization. Multisensor data fusion is a technology to enable combining information from several sources in order to form a unified picture. Uncertainty theories and multisensor data fusion wiley online. 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.
In the framework of dempstershafer evidence theory, the uncertain information is represented not only by mass functions, the fod is also a source of uncertainty, for example, the number of elements in a fod can be changed even if the mass value of each proposition keeps still. Parametric and nonparametric approaches for multisensor data fusion by bing ma cochairs. Multisensor data fusion strategies for advanced driver. Solutions and standards must be agreed on for fusion of data from different sensor principles of different accuracies. Download citation uncertainty theories and multisensor data fusion addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first. First, it introduces bayesian tec hnique for sensor fusion in section 2. New multisensor data fusion method based on probabilistic. An integrated model for robust multisensor data fusion. An introduction to multisensor data fusion david l. Flyeruncertainty theories and multisensor data fusion.
This uncertainty corrupts the quality of the information fusion system. An introduction to multisensor data fusion ieee xplore. Addressing recent challenges and developments in this growing field, multisensor data fusion uncertainty theory first discusses basic questions such as. Full text of handbook of multisensor data fusion see other formats. Construction of a general method for data association 197.
Although most sensors of spaceborne system collect the data through multiple channels of different spectral wave lengths, the fusion process of ds evidence theory usually assumes that each sensorsource is represented by one single channel. Possibility theorys treatment of imperfect data is similar in spirit to probability. Uncertainty theories and multisensor data fusion alain. However, ds evidence theory makes decisions without considering the sensors dierence, which may lead to illogical results. Handling data uncertainty and inconsistency using multisensor. Uncertainty theories and multisensor data fusion by alain. Research article decisionmaking algorithm for multisensor. The nphard multisensor data fusion problem has been converted to a peak picking problem in the grids map. Download it once and read it on your kindle device, pc, phones or tablets.
This method, works based on a clustering technique followed by an mlp predictor. Level 34 data fusion, obviously, builds on level 12 methods. Use features like bookmarks, note taking and highlighting while reading uncertainty theories and multisensor data fusion iste. The information from these three sensing sources was fused to obtain an occupancy profile of the robotic workspace. The dempster shafer evidence theory dset, also known as the evidential theory, is a flexible method in multisensor data fusion 911.
Firstly, gaussian types of fault models and test models are established by observations of sensors. Most data fusion methods depends on system behavior, which do not allow to easily deal with unknown systems. Jun 17, 2014 what is the purpose and the specificity of information fusion processing in multiple sensor systems. Multisensor data fusion algorithms reduce the uncertainty by combining data from. Some of the popular techniques for sensor fusion that are explored extensively in literature include dempstershafer theory for evidential reasoning dempster. Pdf an evidence theory based multi sensor data fusion.
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