Multisensor Data Fusion

Durrant‐Whyte, Hugh; Henderson, Thomas C.

doi:10.1007/978-3-540-30301-5_26

Cited by 105 publications

(63 citation statements)

References 33 publications

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“…Several variants can be found in Bayesian estimation techniques, while the most interesting ones are sequential Bayesian estimation techniques [9] or the fusion of several likelihood functions as in the case of the independent likelihood pool. Robotics, multimedia, or target detection are outline more details on this technique which is quite distinct from ours as it addresses the first order and not the second-order distributions can be found in [10].…”

Section: Distributed Intrusion Detectionmentioning

confidence: 99%

Knowledge Fusion Technique Using Classifier Ensemble by Different Classification Rules

Nandedkar¹

2015

IJIRCCE

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Classification rules are extracted from sample data known as knowledge. If we extract these knowledge in a distributed way, it is necessary to combine or fuse these rules. The task of data fusion is to identify the true values of data items among multiple observed values drawn from different sources of varying reliability. In data mining applications knowledge extraction is splitted into subtasks due to memory or run-time limitations. Again, locally extracted knowledge must be consolidated later because communication overhead should be low. Extracting information from multiple data sources, and reconciling the values so the true values can be stored in a central data repository. But it's a problem of vital importance to the database and knowledge management communities.In a conventional approach extracting knowledge is typically done either by combining the classifiers' outputs or by combining the sets of classification rules but in this paper, I introduce a new way of fusing classifiers at the level of parameters of classification rules. Here its focused around the utilization of probabilistic generative classifiers utilizing multinomial circulations and multivariate ordinary dispersions for the consistent ones. We are using these distributions as hyper distributions or second-order distributions. Fusing of these classifiers are can be done by multiplying the hyper-distributions of the parameters.

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Section: Distributed Intrusion Detectionmentioning

confidence: 99%

Knowledge Fusion Technique Using Classifier Ensemble by Different Classification Rules

Nandedkar¹

2015

IJIRCCE

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“…Each of these problems was addressed separately in the literature considering different approaches. In the case of imperfect data the main approaches followed were the probabilistic [7,8], the evidential [9,10,11], fuzzy reasoning [12,13,14], possibilistic [15,16], rough set theoretic [17,18,19], hybridization [20,14] and random set theoretic [21,22]. In the case of outliers and missing data, the most common approaches are based on sensor validation techniques [23,24,25] and on stochastic adaptive sensor modelling [26].…”

Section: Introductionmentioning

confidence: 99%

Fusing actigraphy signals for outpatient monitoring

Fuster–Garcia¹,

Bresó

Martı́nez-Miranda

et al. 2015

Information Fusion

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ElsevierFuster García, E.; Bresó Guardado, A.; Martínez Miranda, JC.; Rosell-Ferrer, J.; Matheson, C.; García Gómez, JM. (2015) AbstractActigraphy devices have been successfully used as effective tools in the treatment of diseases such as sleep disorders or major depression. Although several efforts have been made in recent years to develop smaller and more portable devices, the features necessary for the continuous monitoring of outpatients require a less intrusive, obstructive and stigmatizing acquisition system. A useful strategy to overcome these limitations is based on adapting the monitoring system to the patient lifestyle and behavior by providing sets of different sensors that can be worn simultaneously or alternatively. This strategy offers to the patient the option of using one device or other according to his/her particular preferences. However this strategy requires a robust multi-sensor fusion methodology capable of taking maximum profit from all of the recorded information. With this aim, this study proposes two actigraphy fusion models including centralized and distributed architectures based on artificial neural networks. These novel fusion methods were tested both on synthetic datasets and real datasets, providing a parametric characterization of the models' behavior, and yielding results based on real case applications. The results obtained using both proposed fusion models exhibit good performance in terms of robustness to signal degradation, as well as a * good behavior in terms of the dependence of signal quality on the number of signals fused. The distributed and centralized fusion methods reduce the mean averaged error of the original signals to 44% and 46% respectively when using simulated datasets. The proposed methods may therefore facilitate a less intrusive and more dependable way of acquiring valuable monitoring information from outpatients.

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“…They are data fusion, mostly done at lower level from similar sensor equipments, information fusion, performed at intermediate level processing and decision fusion, which combines decisions based on separate sensor data. See Most sensor fusion applications in robotics are based on probabilistic methods [47]. These probabilistic methods are based on Bayesian probability rules for processing information by combining apriori and observation information.…”

Section: Sensing Perception and Motion Planningmentioning

confidence: 99%

“…Practically, this can be implemented as Kalman filters, extended Kalman Filters [92,156] or Monte Carlo methods. These probabilistic methods have three basic limitations namely; complexity, inconsistency and model precision [47]. In order to deal with the downsides of the above methods new implementation techniques were proposed that use interval calculus, fuzzy logic, neural networks and theory of evidence also known as Dempster-Shafer (D-S) methods [47].…”

Section: Sensing Perception and Motion Planningmentioning

confidence: 99%

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Human-friendly robotic manipulators: safety and performance issues in controller design

Tadele¹

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Multisensor Data Fusion

Cited by 105 publications

References 33 publications

Knowledge Fusion Technique Using Classifier Ensemble by Different Classification Rules

Knowledge Fusion Technique Using Classifier Ensemble by Different Classification Rules

Fusing actigraphy signals for outpatient monitoring

Human-friendly robotic manipulators: safety and performance issues in controller design

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