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Article

Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs

1
Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
2
Aerospace Engineering, Pennsylvania State University, University Park, PA 16801, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3727; https://doi.org/10.3390/s20133727
Received: 5 May 2020 / Revised: 24 June 2020 / Accepted: 29 June 2020 / Published: 3 July 2020
(This article belongs to the Section Intelligent Sensors)
Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori. View Full-Text
Keywords: Dempster-Shafer; valuation network; joint conditional matrix; optimization; least squares; transition potential; reasoning under uncertainty Dempster-Shafer; valuation network; joint conditional matrix; optimization; least squares; transition potential; reasoning under uncertainty
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MDPI and ACS Style

Dunham, J.; Johnson, E.; Feron, E.; German, B. Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs. Sensors 2020, 20, 3727. https://doi.org/10.3390/s20133727

AMA Style

Dunham J, Johnson E, Feron E, German B. Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs. Sensors. 2020; 20(13):3727. https://doi.org/10.3390/s20133727

Chicago/Turabian Style

Dunham, Joel, Eric Johnson, Eric Feron, and Brian German. 2020. "Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs" Sensors 20, no. 13: 3727. https://doi.org/10.3390/s20133727

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