This paper addresses the use of implication rules (with uncertainty) within the Transferable Belief Model (TBM)
where the rules convey knowledge about relationships between two frames of discernment. Technical challenges
include: a) computational scalability of belief propagation, b) logical consistency of the rules, and c) uncertainty
of the rules. This paper presents a simplification of the formalism developed by Ristic and Smets for incorporating
uncertain implication rules into the TBM. By imposing two constraints on the form of implication rules, and
restricting results to singletons of the frame of discernment, we derive a belief function that can be evaluated in
polynomial time.
KEYWORDS: Taxonomy, Kinematics, Sensors, Photonic integrated circuits, Transform theory, Information fusion, Data fusion, Detection and tracking algorithms, Data modeling, Information theory
This paper addresses the problem of multi-source object classication in a context where objects of interest are
part of a known taxonomy and the classication sources report at varying levels of specicity. This problem
must consider several technical challenges: a) support fusion of heterogeneous classication inputs, b) provide a
computationally scalable approach that accommodates taxonomy's with thousands of leaf nodes, and c) provide
outputs that support tactical decision aides and are suitable inputs for subsequent fusion processes. This paper
presents an approach that employs the Transferable Belief Model, Pignistic Transforms, and Bayesian Fusion to
address these challenges.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.