One can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Sep 05, 2017 hui, kar hoou, lim, meng hee, and leong, salman. Introduction in his 1976 book, glenn shafer introduced the theory of evidence, later referred to as the dempster shafer theory dst. Dempstershafer evidence theory dset is a flexible and popular paradigm for multisource data fusion in wireless sensor networks wsns. This paper proposed a novel 3d palmprint recognition algorithm by combining 3d palmprint features using ds fusion theory. This paper presents a novel and easy implementing method computing masses from the hundreds of pieces of data collected by a wsn. Dempster shafer based sensor fusion approach for machinery fault diagnosis. Proceedings of the asme 2017 power conference joint with icope17 collocated with the asme 2017 11th international conference on energy sustainability, the asme 2017 15th international conference on fuel cell science. In the absence of information, these methods must then make. Multisensor fusion based on multiple classifier systems. Moreover, it should be remove redundant packets in order to lower the average energy consumption.
Abstractthis paper proposes an approach to the fusion of multimodal sensor data for the purpose of personnel intrusion detection. Multiplesensor system applications, benefits, and design considerations. Imse 830 engine fault diagnosis based on multisensor. Thirdly, the belief function of the mean curvature. Combination of evidence in dempstershafer theory, by. Mel siegel, chair daniel siewiorek jie yang wolfgang grimm, robert bosch corporation the robotics institute. Dempster shafer evidential theory, a probabilitybased data fusion classification algorithm, is useful when the sensors or more generally, the information sources contributing information cannot associate a 100 percent probability of certainty to their output decisions.
Pdf sensor fusion using dempstershafer theory researchgate. The remote sensing data used are derived from the aster sensor and are rec. In particular, we investigate the use of dempstershafer based data fusion model and present techniques for processing of visual sensor data to facilitate the use of dempstershafer model. Transferable belief model tbm is an interpretation of dempster shafer s theory based on the work ofshafer1976. Although sensors may be of good quality and provide accurate readings, several factors may affect the signal level received by a sensor at a given location elouedi et al. This naturally leads us to choose the dempster shafer approach as our first sensor fusion implementation algorithm. 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. The concept of multisensor information fusion model based on the dempster shafer theory is introduced. Novel paradigm for constructing masses in dempstershafer. Eddy current and ultrasound data fusion using dempster shafer theory author. Sensor fusion using dempstershafer theory for contextaware hci. Dempstershafer sensor fusion algorithm the bayesian theory is the canonical method for statistical inference problems. In this paper, we apply the dempster shafer ds theory of evidence 31 to model imperfect sensor information since it can be used to model common types of sensor uncertainty e.
Multiple sensor fusion for detection, classification and tracking of moving objects in driving environments. Mar 22, 2012 the dempster shafer theory, a generalization of the bayesian theory, is based on the idea of belief and as such can handle ignorance. Novel data fusion algorithm based on eventdriven and. Multisensor image segmentation using dempstershafer.
Cloud presence prediction using dempster shafer evidence theory this section will focus on dempster shafer evidence theory applications in multiple sensor environments and present our implementation for the cloud presence prediction. Engine fault diagnosis based on multisensor information fusion using dempstershafer evidence theory imse 830fuzzy sets theory behnam malmir spring 2016 kansas state university key sentences engine diagnostics is a typical multisensor fusion problem. Dempstershafer evidential theory, a probabilitybased data fusion classification algorithm, is useful when the sensors or more generally, the information sources contributing information cannot associate a 100 percent probability of certainty to their output decisions. We present a dempstershafer evidence theory formulation of the engine fault diagnosis problem in terms of fault frame of discernment and mass functions, evidence combination, decision making rules, and fusion performance evaluation.
Sensor and data fusion architectures and algorithms. Put another way, it is a way of representing epistemic plausibilities but it can yield answers which contradict those arrived at using probability theory. A fusion approach using dempstershafer theory and bayesian learning article in information fusion 104. However, it is more common to use the term in the wider sense of the same general approach, as adapted to specific kinds of. Introduction until now, military industry designers have developed sensors in a stand alone sensor context. Dempstershaferbased sensor fusion approach for machinery. Secondly, two types of unique features, including mean curvature feature and gaussian curvature feature, are extracted.
The dempstershafer decision theory is essentially a generalized bayesian statistical theory. Both the flexibility and the difficulty consist in defining the probability mass function. Data and information fusion, bayesian, dempstershafer, fuzzy logic, neural networks, visual. Application of the dempstershafer theory to the classification of pixels from aster satellite images and spectral. 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. 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 comparison with. Contextdependent combination of sensor information in. Sensor fusion using dempstershafer theory of evidence in autonomous robot navigation 9 direction is also continuous is type 2. Pdf sensor fusion using dempstershafer theory for context. Dempstershafer theory for sensor f usion robin murph y. A new multisensor information fusion model using dempster. When a new event occurs, the nodes in the neighborhood of the event sense and then send many packets to the sink node.
Dempstershafer theory for data fusion applications amandine bellenger 1, sylvain gatepaille 1 1eads, defence and security information processing, control and cognition department parc daffaires des portes 27106 valdereuil france amandine. Shafer theory of evidence using a large scale group decision making approach emrah koksalmis hezarfen aeronautics and space technologies institute, national defense university, istanbul, turkey. Faulttolerant interval estimation fusion by dempster. Dempster shafer sensor fusion for autonomously driving. Shafer theory of evidence algorithm as our baseline sensor fusion. The concept of multisensor information fusion model based on the dempstershafer theory is introduced. Sensor data fusion using dsm theory for activity recognition. Dempstershafer fusion for personnel detection application of dempstershafer theory with ultrasonic microdoppler and pir sensors. Eddy current and ultrasound data fusion using dempster. Dempstershafer theory, with an example given of fusion using the cornerstone of the theory known as dempsters rule. In this paper, we apply the dempstershafer ds theory of evidence 31 to model imperfect sensor information since it can be used to model common types of sensor uncertainty e. The feasibility of this idea is demonstrated via a meetingparticipant.
A curve has curvature continuity as well as tangent direction continuity is called type 3. Reliable contextual information of remotely monitoredpatients should be generated to prevent hazardous situationsand to provide pervasive services in. The evidence entropy of the fusion process is computed for each case using eq. Often used as a method of sensor fusion, dempstershafer theory is based on two ideas. Mobile robot selflocalization and local map alignment with a. The use of dempstershafer theory helps to reduce uncertainty and imprecision in sensor representation and increases reliability. Its new feature is that it allows distributing support for a proposition e. Let the frame of discernment or hypotheses space w be the set of all possible so. An introduction to bayesian and dempstershafer data fusion. Data from four types of sensor data were collected by a mobile robot and fused with the. The resulting information of the architecture is combined using similar sensor subset and dissimilar sensor subset. 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. The use of locally weighted regression for the data fusion.
The aim of this paper is to investigate how to use the contextual knowledge in order to improve the fusion process. Fusionbased surveillance wsn deployment using dempster. A scheme for robust distributed sensor fusion based on average consensus. Sensors free fulltext bearing fault diagnosis based on a. 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. Mobile robot selflocalization and local map alignment. This data fusion method is based on the idea of belief and as such can handle ignorance.
A dempstershafer method for multisensor fusion by bethany. One possible way of using the theory of evidence is the following. When all of the required information is available, many data fusion methods provide a solid approach. The results of the proposed method were improved by comparing with the ordinary methods. Furthermore, the reliability of a sensor can be updated by the fusion center using methods such as elouedi et al. Towards having computers understand human usersontext. Multiple sensor fusion has been a topic of research since long. In the absence of information, these methods must then make assumptions about the sensor data.
In particular, the use of dempstershafer theory of evidence within markov models has brought some keys to several challenging difficulties that the conventional hidden markov models cannot handle. Mobile robot selflocalization and loca l map alignment with a dempster shafer sensor fusion algorithm hyunki lee, xingyong song and hyungsuck cho department of mechanical engineering, korea advanced ins titute of science and technology, daejeon, korea tel. The term k is called the inconsistency of the fusion and can be interpreted as a measure of conflict. Dempstershafer theory for sensor f usion in autonomous mobile rob ots robin r. Find support for a specific problem on the support section of our website. Sensor fusion using dempstershafer theory for contextaware hci conference paper pdf available in conference record ieee instrumentation and measurement technology conference 1. A benchmark scenario has been selected to quantitatively demonstrate the capability of the attribute fusion algorithm. In this work we show that it can be viewed as a bayesian fusion algorithm and derive it using bayesian arguments. Sensor data fusion for contextaware computing using dempster shafer theory huadong wu cmuritr0352 submitted in partial fulfillment of the requirements for the degree of doctor of philosophy in robotics thesis committee. Sensor data fusion for contextaware computing using. Expert systemsdempstershafer theory wikibooks, open books. Sensor data fusion for accurate cloud presence prediction. Sensor data fusion for accurate cloud presence prediction using.
Dempstershafer theory is based on the nonclassical idea of mass as opposed to the wellunderstood probabilities of bayes theory. 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. For example, the calculatede m1 entropy of evidence for sensor s 1 is 0. Dempstershaferbased sensor fusion approach for machinery fault diagnosis. Based on the fused occupancy grid map, dynamic obstacles are tracked to give a velocity estimate without the need of any object or. Multiple sensor fusion for detection, classification and. Firstly, the structured light imaging is used to acquire the 3d palmprint data. It also makes use of the ds weight of con ict metric to prevent the robot from acting on faulty observations. So the bayesian fusion simply is a normalized product. Alina bruma, nicoleta iftimie, rozina steigmann, adriana savin, raimond grimberg subject.
We build this paper on two imtc2002 papers, where the dempstershafer theory of evidence was shown to be a practical approach to implementing the sensor. On precision bound of distributed faulttolerant sensor. Braun j 2000 dempstershafer theory and bayesian reasoning in multisensor data fusion,sensor fusion. Dempstershafer decisionmaking dempstershafer example combination of evidence in dempstershafer theory, by kari sentz relation between fuzzy logic, neural networks, and dempstershafer reinforcement learning control rule base systems petri nets. Multiple sensor fusion for detection, classification and tracking of moving objects in driving environments r. The dempstershafer ds theory provides an efficient framework to implement multisensor data fusion.
Sensor fusion for contextaware computing using dempster. In proceedings of the 19th ieee instrumentation and measurement technology conference, 2002 imtc02. Dempster in the context of statistical inference, the theory was later. The sfx hitecture is a generic sensor fusion system for autonomous mobile rob ots, suitable for a wide v ariet y of. The latest developments in markov models theory and their corresponding computational techniques have opened new rooms for image and signal modeling. 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. Panigrahi 7 investigated a fusion approach using dempstershafer theory and bayesian learning, the positive point in this approach that the architecture has been kept flexible so that new rules.
How does one generalize this fusion to take the presence of clouds into account. The kalman filter is traditionally viewed as a predictioncorrection filtering algorithm. Sensors free fulltext bearing fault diagnosis based. The dempstershafer theory, a generalization of the bayesian theory, is based on the idea of belief and as such can handle ignorance. A data fusion algorithm based on eventdriven and dempstershafer evidence theory is proposed in this paper to reduce data packet quantities and reserve energy for wireless sensor networks upon detecting abnormal data. 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. The authors proposed an improved dempstershafer theory for the hybrid classifier ensemble approach.
Faulttolerant interval estimation fusion by dempstershafer. Yet, most do not have a good way of dealing with ignorance. Sensor data fusion for contextaware computing using dempster. Dempstershafer evidential theory, sensor data fusion, antiair warfare, attribute fusion, identity estimation. Six experiments using this dempstershafer framework are presented.
Engine fault diagnosis based on multisensor information. Multisensor image segmentation using dempstershafer fusion. Proceedings of the asme 2017 power conference joint with icope17 collocated with the asme 2017 11th international conference on energy sustainability, the asme 2017 15th international conference on fuel cell science, engineering and technology, and the asme 2017. In section 2, we introduce some preliminary concepts of the evidence theory. In a narrow sense, the term dempstershafer theory refers to the original conception of the theory by dempster and shafer. The dempstershafer theory of evidence has uncertainty management and inference mechanisms analogous to our human reasoning process. Often used as a method of sensor fusion, dempster shafer theory is based on two ideas. Acknowledgements i am highly thankful to my director of thesis and my colleges for all the advices and help. Experiments with four types of sensor data collected by a. The fusion result is a discrete value or a label, which is determined by the corresponding maximum probability values. Faulttolerant interval estimation fusion by dempstershafer theory baohua li yunmin zhu. Sensor data fusion for contextaware computing using dempstershafer theory huadong wu cmuritr0352 submitted in partial fulfillment of the requirements for the degree of doctor of philosophy in robotics thesis committee. Such circumstances need their networks to be simultaneously reliable and eventdriven. This paper discusses the relationship between dempster shafer theory and the classical bayesian method, describes our sensor fusion research work using dempster shafer theory in comparison with the weighted sum of probability method.
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