The kalman filter is traditionally viewed as a predictioncorrection filtering algorithm. Dempstershafer theory is based on the assumption that these sources are independent. Dempstershafer theory for sensor f usion in autonomous mobile rob ots robin r. Moreover, a lot of important and critical decisions are made based. Sensor fusion using dempstershafer theory of evidence in autonomous robot navigation 9. The evidence entropy of the fusion process is computed for each case using eq. In this paper, it is proposed to apply the dempstershafer theory dst or the theory of evidence to map vegetation, aquatic and mineral surfaces with a view to detecting potential areas of observation of outcrops of geological formations rocks, breastplates, regolith, etc. Dempster in the context of statistical inference, the theory was later. Pdf sensor fusion using dempstershafer theory for context. Now, to combine inputs from different sensors at the decision level to. The dempstershafer theory dst is a mathematical theory of evidence.
We begin with an outline of bayes theory, using it to discuss wellknown quantities such as priors, likelihood and posteriors, and we provide the basic bayesian. But it is not explored much as yet in the context of internet of things. Dempstershafer theory, data classification, dempsters rule of combination. Introduction in his 1976 book, glenn shafer introduced the theory of evidence, later referred to as the dempstershafer theory dst. An application of dempstershafer fusion theory to lithium. A fault tolerant sensor fusion based on the dempster shafer theory to take the uncertainty of the pose estimate into account is discussed and compared using an example, although not implemented on the test vehicle. Pdf sensor fusion using dempstershafer theory researchgate.
Dempstershafer theory gives a rule for calculating the confidence measure of each state, based on data from different evidences. Dempster shafer sensor fusion for autonomously driving. This article discusses dempstershafer ds theory in terms of its utility for sensor fusion for autonomous mobile robots. Advances in intelligent systems and computing, vol 769. Sensor fusion using dempstershafer theory for contextaware hci conference paper pdf available in conference record ieee instrumentation and measurement technology conference 1. Then, according to this model and its preference for sensing tasks, we proposed the sensing task allocation algorithm based on dempstershafer ds. However, to discern static and moving obstacles, the dempstershafer approach requires manual tuning of parameters dependent on the situation and sensor types. Information fusion with dempstershafer evidence theory. Dempstershafer evidence theorybased multifeature learning and fusion method for nonrigid 3d model retrieval. Application of dempstershafer theory to a novel control. Dempstershafer theory for sensor fusion in autonomous. Dempstershafer evidence theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty.
The dempstershafer theory of evidence has uncertainty management and inference mechanisms. The paper concludes with a discussion of the results and in particular future directions of the work and how enhanced data sets could aid the evaluation of the approach. In presence of an overload of information and an unknown environment, the reliability of the sources of information or the sensors is usually unknown and thus cannot be used to re ne the fusion process. Sensor fusion methods are highly dependent on an accurate pose estimate, which can not be guaranteed in any case. After performing evidence validity, the dempstershafer fusion process is invoked to update belief values for the two gender classes. Fnacytology and geneexpression data fusion methodology using dempstershafer theory of evidence the nine independent parameters of fnac data are. The implicit handling of uncertainty and ambiguous classes in this approach are particularly useful in applications of multimodal sensor fusion. The weight of a mass function is determined using a set of defect features which in turn is fused using the dempster shafer evidence combination rule. Fusion background the method outlined herein is developed based on the dempstershafer theory of evidence.
It exploits two little used components of ds theory. In this paper, we apply the dempstershafer ds theory of evidence. Scorelevel multibiometric fusion based on dempstershafer. Since then, nearly four h undred pap ers on the theory and practice of ds reasoning has b een. However, it is more common to use the term in the wider sense of the same general approach, as adapted to specific kinds of. Citeseerx document details isaac councill, lee giles, pradeep teregowda. This rule has been used extensively in information fusion applications. Pdf contextsensing for contextaware hci challenges the traditional sensor. This naturally leads us to choose the dempstershafer approach as our first sensor fusion implementation algorithm. This paper discusses the relationship between dempstershafer theory and the classical bayesian method, describes our sensor fusion research work using dempstershafer theory in. I will try to explain belief and plausibility terms in more general format. Dempstershafer theory is based on the nonclassical idea of mass as opposed to the wellunderstood probabilities of bayes theory. We build this paper on two imtc2002 papers, where the dempster.
Ieee instrumentation and measurement technology conference anchorage, ak, usa, 2123 may 2002 sensor fusion using dempstershafer theory huadong wu 1, mel siegel2contact author, rainer stiefelhagen3,jieyang4 1,2robotics institute, carnegie mellon university 5000 forbes ave. Dempstershafer theory provides a complete framework for combining weak evidences from multiple sources. Why demspter shafer theory, why we use it and where is it most used will be addressed. Pdf contextsensing for contextaware hci challenges the traditional sensor fusion methods with dynamic sensor configuration and. Ieee transactions on systems, man and cybernetics part c. A weighted combination method for conflicting evidence in. An example based on simulated sensor data illustrates this application of dempstershafer theory. Sensor fusion using dempstershafer theory of evidence in autonomous robot navigation 9 direction is also continuous is type 2. Sensor fusion using dempstershafer theory for contextaware hci. Fusion of fnacytology and geneexpression data using.
This paper discusses the relationship between dempstershafer theory and the classical bayesian method, describes our sensor fusion research work using dempstershafer theory in comparison with the weighted sum of probability method. Sensor data fusion for accurate cloud presence prediction. Sensor fusion using dempstershafer theory the robotics. Sensor fusion for contextaware computing using dempster. Fault detection in sensor nodes is a pertinent issue that has been an important area of research for a very long time.
Subsequent valid evidence is combined with the previous belief values using the dempstershafer fusion scheme. With regard to ds theory, sensor fusion is actively used in. In this work we show that it can be viewed as a bayesian fusion algorithm and derive it using bayesian arguments. In this video i will explain you about dempster shafer theory in artificial intelligence and how you have to solve the plaussibility case. Dempstershafer evidence theory is employed for this purpose. An introduction to bayesian and dempstershafer data fusion. An application of dempstershafer fusion theory to lithiumion battery prognostics and health management john weddington university of south carolina follow this and additional works at.
Dempstershafer theory provides a sensor fusion framework that autonomously accounts for obstacle occlusion in dynamic, urban environments. For example, the calculatede m1 entropy of evidence for sensor s 1 is 0. The first set of belief values are obtained from the first valid evidence observed. Use of evidence theory to combine the outputs of two or more primary classifiers to improve overall classification performance is reported in 18. Dempstershafer theory, with an example given of fusion using the cornerstone of the theory known as dempsters rule. Low level fusion of imagery based on dempstershafer theory. Further, we model the relationship between agents using multiattribute fusion. Sensory task assignment based on dempstershafer theory and multiattribute fusion in mobile sensor networks.
Modified dempstershafer ds evidence theory can handle. Dempstershafer evidential reasoning is proposed in this article. Dempstershafer fusion based gender recognition for. The feasibility of this idea is demonstrated via a meetingparticipant. Multimetric idss combine information from two or more layers of the protocol stack. Engine fault diagnosis based on multisensor information. Application of dempstershafer theory with ultrasonic microdoppler and pir sensors 5a. This approach allows combination of sensor outputs that effectively classify different sets of objects with varying levels of granularity. The key contribution of this thesis is introducing the dempstershafer theory of evidence as a generalizable sensor fusion solution to overcome the typical context sensing difficulties, wherein some of the available information items are subjective. Static weighting and kalman filterlike dynamic weighting.
A cornerstone in this formalism is dempsters rule, which provides a mechanism for combining belief functions representing independent pieces of evidence. Three drawbacks of ds theory computational intractability, weak assumptions of statistical independence, and counterintuitive averaging of strongly biased evidence are eliminated by applying ds theory within the constraints of the model. Inasmuch as this paper is a continuation and expansion of the second of these two papers, we need refer back to them in order to make the background work clear. Distributed data fusion in the dempstershafer framework. A fusion methodology based on dempstershafer evidence.
Citeseerx sensor fusion using dempstershafer theory. Data classification using the dempstershafer method arxiv. Thus a new automatic fusion methodology using different sources of information different feature sets is presented here. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing 69. A curve has curvature continuity as well as tangent direction continuity is called type 3. Decision making in data fusion using dempstershafers theory. The fusion of these different sources of information can provide an opportunity to develop more efficient biometric system compared when using a feature vector. Paradox elimination in dempstershafer combination rule with. Abstract context sensing for contextaware hci challenges traditional sensor fusion methods with its requirements for 1 adaptability to a constantly changing sensor suite and 2 sensing quality commensurate with human perception. Abstractdempstershafer theory is a formal framework for reasoning and decisionmaking under uncertainty. In a previous paper 1, the authors have investigated dierent techniques to evalu. The theory of belief functions, also referred to as evidence theory or dempstershafer theory dst, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories. We build this paper on two previous papers, sensor fusion for context understanding 1 and sensor fusion using dempstershafer theory 2, presented at imtc2002. Contextdependent combination of sensor information in dempster.
Contextsensing for contextaware hci challenges the traditional sensor fusion methods with dynamic sensor configuration and measurement requirements commensurate with human perception. The dempstershafer decision theory is considered a generalized bayesian theory. Dempsters rule of combination has been used as sensor fusion strategy, as given in equations 4 and 5. In remote sensing applications, the expected number of classes may be large, in particular for agricultural areas. A fusion approach using dempstershafer theory and bayesian learning.
Dempstershafer theory for sensor f usion robin murph y. Part of theelectrical and computer engineering commons this open access thesis is brought to you by scholar commons. In a narrow sense, the term dempstershafer theory refers to the original conception of the theory by dempster and shafer. Author links open overlay panel suvasini panigrahi a amlan kundu a shamik sural a a. This data fusion method is based on the idea of belief and as such can handle ignorance.
1397 1245 108 491 739 540 443 1255 750 3 656 376 1004 403 586 463 1283 276 1402 1129 626 1178 510 965 1557 277 49 1263 1000 824 1342 205 930 1291 1327 1015 1457 1330 409 1229 1111 897 1181