Paper Info
Title
Evidence supporting measure of similarity for reducing the complexity in information fusion
Abstract
This paper presents a new method for reducing the number of sources of evidence to combine in order to reduce the complexity of the fusion processing. Such a complexity reduction is often required in many applications where the real-time constraint and limited computing resources are of prime importance. The basic idea consists in selecting, among all sources available, only a subset of sources of evidence to combine. The selection is based on an evidence supporting measure of similarity (ESMS) criterion which is an efficient generic tool for outlier sources identification and rejection. The ESMS between two sources of evidence can be defined using several measures of distance following different lattice structures. In this paper, we propose such four measures of distance for ESMS and we present in details the principle of Generalized Fusion Machine (GFM). Then we apply it experimentally to the real-time perception of the environment with a mobile robot using sonar sensors. A comparative analysis of results is done and presented in the last part of this paper.
Year
DOI
Venue
2011
10.1016/j.ins.2010.10.025
Inf. Sci.
Keywords
Field
DocType
generalized fusion machine,real-time constraint,basic idea,complexity reduction,fusion processing,outlier sources identification,information fusion,efficient generic tool,comparative analysis,different lattice structure,real-time perception,lattice,mobile robot,distance,real time
Prime (order theory),Data mining,Robot perception,Outlier,Sonar,Reduction (complexity),Artificial intelligence,Information fusion,Mobile robot,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
181
10
0020-0255
Citations 
PageRank 
References 
17
0.85
16
Authors
4
Name
Order
Citations
PageRank
Xinde Li15011.00
Jean Dezert277761.59
Florentin Smarandache3728104.92
Xinhan Huang411419.04