Branch Mathematics and Statistics Faculty and Staff Publications
Document Type
Article
Publication Date
2011
Abstract
This paper proposes a new solution for reducing the number of sources of evidence to be combined in order to diminish the complexity of the fusion process required in some applications where the real-time constraint and strong computing resource limitation are of prime importance. The basic idea consists in selecting, among the whole set of sources of evidence, only the biggest subset of sources which are not too contradicting based on a criterion of Evidence Supporting Measure of Similarity (ESMS) in order to process solely the coherent information received. The ESMS criterion serves actually as a generic tool for outlier source identification and rejection. Since the ESMS between several belief functions can be defined using several distance measures, we browse the most common ones in this paper and we describe in detail the principle of our Generalized Fusion Machine (GFM). The last part of the paper shows the improvement of the performances of this new approach with respect to the classical one in a real-data based and real-time experiment for robot perception using sonar sensors.
Publication Title
Information Sciences
Volume
181
First Page
1818
Last Page
1835
Language (ISO)
English
Keywords
Information fusion, Belief function, Complexity reduction, Robot perception, DSmT, Measure of similarity, Distance, Lattice
Recommended Citation
Li, Xinde; Jean Dezert; Florentin Smarandache; and Xinhan Huang.
"Evidence Supporting Measure of Similarity for Reducing the Complexity in Information Fusion."
Information Sciences
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Included in
Aerospace Engineering Commons, Aviation Commons, Digital Communications and Networking Commons, Logic and Foundations Commons, Other Computer Engineering Commons, Other Engineering Commons, Other Mathematics Commons
