NDLI: Incorporation of Partially Observable Evidence Into an Evidence Accrual Data Fusion Technique

Content Provider	IEEE Xplore Digital Library
Author	Stubberud, S.C. Kramer, K.A.
Copyright Year	2007
Description	Author affiliation: Rockwell Collins, Poway (Stubberud, S.C.)
Abstract	In many sensor fusion problems, such as level 1 (object refinement), level 2 (situational assessment), or level 3 (impact assessment), observations frequently provide indirect, rather than direct, evidence. In such cases, the measurements affect the evidence or level of interest through a functional relationship. Often, these observations can be considered partially observable, such as the relationship between a bearings-only measurement and target position. A general evidence accrual system that incorporates these partially- observable indirect observations into the evidence generation is developed. The technique, based on the concepts of first- order and reduced-order observer theory, can incorporate both observation quality and level of doctrine understanding into the uncertainty measure of the evidence. Unlike a Bayesian taxonomy, the proposed method does not rely upon the strict probabilistic underpinnings, but instead uses a network structure with links and propagation of evidence. In this work, proof of capability is demonstrated by applying the technique to a Level 1 classification fusion problem where the observations are target attributes. The technique, based upon an existing evidence accrual algorithm, uses a fuzzy Kalman filter to inject new evidence into the nodes of interest to modify the level of evidence. The fuzzy Kalman filter allows for the level of evidence to incorporate an uncertainty or quality measure into the report.
Starting Page	251
Ending Page	256
File Size	260835
Page Count	6
File Format	PDF
ISBN	9781424415014
DOI	10.1109/ISSNIP.2007.4496852
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-03
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Bayesian methods Taxonomy Measurement uncertainty Sensor fusion Position measurement Sensor phenomena and characterization Data engineering State estimation Fuzzy systems
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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