scholarly journals Erratum to: A Novel Methodology for Target Classification Based on Dempster-Shafer Theory

2017 ◽  
pp. E1-E2
Author(s):  
Hasan Ihsan Turhan ◽  
Mubeccel Demirekler ◽  
Melih Gunay
Keyword(s):  
Target Classification ◽  
Dempster Shafer Theory ◽  
Shafer Theory

scholarly journals Radar target classification using improved Dempster–Shafer theory

2019 ◽  
Vol 2019 (21) ◽  
pp. 7872-7875
Author(s):  
Parth Mehta ◽  
Anindita De ◽  
Dayalan Shashikiran ◽  
Kamla Prasan Ray
Keyword(s):  
Radar Target ◽  
Target Classification ◽  
Dempster Shafer Theory ◽  
Shafer Theory

Decision Support System for Advanced Traffic Management Through Data Fusion

2002 ◽  
Vol 1804 (1) ◽  
pp. 173-178 ◽  
Author(s):  
Lawrence A. Klein ◽  
Ping Yi ◽  
Hualiang Teng
Keyword(s):  
Data Fusion ◽  
Traffic Management ◽  
Information Sources ◽  
Partial Information ◽  
Weather Conditions ◽  
Field Testing ◽  
Traffic Operations ◽  
Dempster Shafer Theory ◽  
Classification Technique ◽  
Shafer Theory

The Dempster–Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempste–Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster’s rule is applied to determine the most probable event—as that with the largest probability based on the information obtained from all contributing sources. The Dempster–Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

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

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3727
Author(s):  
Joel Dunham ◽  
Eric Johnson ◽  
Eric Feron ◽  
Brian German
Keyword(s):  
Sensor Fusion ◽  
A Priori ◽  
Evidential Reasoning ◽  
Unmanned Aerial Systems ◽  
Sufficient Information ◽  
User Input ◽  
Transition Potential ◽  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Aerial Systems

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.

An application of Dempster-Shafer theory to the assessment of biogas technology

Energy ◽  
1992 ◽  
Vol 17 (3) ◽  
pp. 205-214
Author(s):  
Aurora A. Kawahara ◽  
Peter M. Williams
Keyword(s):  
Dempster Shafer Theory ◽  
Shafer Theory ◽  
Biogas Technology

Geospatial Modeling Using Dempster–Shafer Theory

2017 ◽  
Vol 47 (6) ◽  
pp. 1551-1561 ◽  
Author(s):  
Paul A. Elmore ◽  
Frederick E. Petry ◽  
Ronald R. Yager
Keyword(s):  
Geospatial Modeling ◽  
Dempster Shafer Theory ◽  
Shafer Theory
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