Fuzzy modeling of measurement data acquired from physical sensors

Mauris, Gilles; Lasserre, Virginie; Foulloy, Laurent

doi:10.1109/19.893256

Cited by 55 publications

(22 citation statements)

References 8 publications

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“…They provide much tighter probability bounds than Chebyshev and Camp-Meidel inequalities for symmetric densities with bounded support. This setting is adapted to the modelling of sensor measurements [115]. These results are extended to more general distributions by Baudrit et al, [7], and provide a tool for representing poor probabilistic information.…”

Section: Probability-possibility Transformationsmentioning

confidence: 99%

Possibility Theory and Its Applications: Where Do We Stand?

Dubois

Prade

2015

Springer Handbook of Computational Intelligence

141

101

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This paper provides an overview of possibility theory, emphasizing its historical roots and its recent developments. Possibility theory lies at the crossroads between fuzzy sets, probability and non-monotonic reasoning. Possibility theory can be cast either in an ordinal or in a numerical setting. Qualitative possibility theory is closely related to belief revision theory, and common-sense reasoning with exception-tainted knowledge in Artificial Intelligence. Possibilistic logic provides a rich representation setting, which enables the handling of lower bounds of possibility theory measures, while remaining close to classical logic. Qualitative possibility theory has been axiomatically justified in a decision-theoretic framework in the style of Savage, thus providing a foundation for qualitative decision theory. Quantitative possibility theory is the simplest framework for statistical reasoning with imprecise probabilities. As such it has close connections with random set theory and confidence intervals, and can provide a tool for uncertainty propagation with limited statistical or subjective information.

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Section: Probability-possibility Transformationsmentioning

confidence: 99%

Possibility Theory and Its Applications: Where Do We Stand?

Dubois

Prade

2015

Springer Handbook of Computational Intelligence

141

101

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“…Then the statistical sensor characteristics function p x i is transferred to the sensor observation π i by the truncated triangular probability-possibility transform Mauris et al, 2000), which also allows for the transfer of Gaussian, triangular and Laplacian pdfs.…”

Section: Sensor Defect Detectionmentioning

confidence: 99%

“…The foundations of the truncated triangular probability-possibility transform are not included in this contribution since it is only applied and Lasserre et al (2000) and Mauris et al (2000) provided excellent introductions to it.…”

Section: Sensor Defect Detectionmentioning

confidence: 99%

Sensor defect detection in multisensor information fusion

Ehlenbröker¹,

Mönks²,

Lohweg³

2016

J. Sens. Sens. Syst.

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Abstract. In industrial processes a vast variety of different sensors is increasingly used to measure and control processes, machines, and logistics. One way to handle the resulting large amount of data created by hundreds or even thousands of different sensors in an application is to employ information fusion systems. Information fusion systems, e.g. for condition monitoring, combine different sources of information, like sensors, to generate the state of a complex system. The result of such an information fusion process is regarded as a health indicator of a complex system. Therefore, information fusion approaches are applied to, e.g., automatically inform one about a reduction in production quality, or detect possibly dangerous situations. Considering the importance of sensors in the previously described information fusion systems and in industrial processes in general, a defective sensor has several negative consequences. It may lead to machine failure, e.g. when wear and tear of a machine is not detected sufficiently in advance. In this contribution we present a method to detect faulty sensors by computing the consistency between sensor values. The proposed sensor defect detection algorithm exemplarily utilises the structure of a multilayered group-based sensor fusion algorithm. Defect detection results of the proposed method for different test cases and the method's capability to detect a number of typical sensor defects are shown.

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“…In order to perform effectively in the scenarios with limited computation capabilities, possibility theory prefers to adopt. In this paper, the basic possibility theory for fuzzy modeling of measurement data originally developed in [21] is adopted. This method avoids the complex associated computations and the further treatment of the propagation of information is easier to realize, especially because of the simple parameterized shape of possibility distribution.…”

Section: Fuzzy Modeling For Measurement Selectionmentioning

confidence: 99%

“…The aim of the fuzzy modeling proposed in [21] was to use the sensor measurement data for derivation and fusion; however, in this paper, the fuzzy modeling is introduced for measurement condition estimation. Thus, a new application scenario of fuzzy modeling is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy modeling, maximum likelihood estimation, and Kalman filtering for target tracking in NLOS scenarios

Yan

2014

EURASIP J. Adv. Signal Process.

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To mitigate the non-line-of-sight (NLOS) effect, a three-step positioning approach is proposed in this article for target tracking. The possibility of each distance measurement under line-of-sight condition is first obtained by applying the truncated triangular probability-possibility transformation associated with fuzzy modeling. Based on the calculated possibilities, the measurements are utilized to obtain intermediate position estimates using the maximum likelihood estimation (MLE), according to identified measurement condition. These intermediate position estimates are then filtered using a linear Kalman filter (KF) to produce the final target position estimates. The target motion information and statistical characteristics of the MLE results are employed in updating the KF parameters. The KF position prediction is exploited for MLE parameter initialization and distance measurement selection. Simulation results demonstrate that the proposed approach outperforms the existing algorithms in the presence of unknown NLOS propagation conditions and achieves a performance close to that when propagation conditions are perfectly known.

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Fuzzy modeling of measurement data acquired from physical sensors

Cited by 55 publications

References 8 publications

Possibility Theory and Its Applications: Where Do We Stand?

Possibility Theory and Its Applications: Where Do We Stand?

Sensor defect detection in multisensor information fusion

Fuzzy modeling, maximum likelihood estimation, and Kalman filtering for target tracking in NLOS scenarios

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