Branch Mathematics and Statistics Faculty and Staff Publications

Document Type

Article

Publication Date

2011

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.

Publisher

Elsevier Inc.

Publication Title

Information Science

Volume

181

First Page

1818

Last Page

1835

DOI

doi:10.1016/j.ins.2010.10.025

Language (ISO)

English

Keywords

Information fusion, belief functions, DSmT, complexity reduction, measure of similarity, distance

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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