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Data association in multi‐target detection using the transferable belief model
Author(s) -
Ayoun André,
Smets Philippe
Publication year - 2001
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.1054
Subject(s) - association (psychology) , data association , computer science , representation (politics) , set (abstract data type) , data mining , artificial intelligence , data set , machine learning , psychology , politics , probabilistic logic , political science , law , psychotherapist , programming language
In the transferable belief model, a model for the quantified representation of beliefs, some masses can be allocated to the empty set. It reflects the conflict between the sources of information. This quantified conflict can be used in order to solve the problem of data association in a multi‐target detection problem. We present and illustrate the procedure by studying an example based on the detection of submarines. Their number and the association of each sensor to a particular source are determined by the procedure. © 2001 John Wiley & Sons, Inc.