Evidence supporting measure of similarity for reducing the complexity in information fusion

Authors:
Xinde Li;Jean Dezert;Florentin Smarandache;Xinhan Huang
Affiliations:
Key Laboratory of Measurement and Control of CSE (School of Automation, Southeast University), Ministry of Education, Nanjing 210096, China;ONERA (The French Aerospace Lab), 29 Av. de la Division Leclerc, 92320 Chítillon, France;Chair of Mathematics and Sciences Department, University of New Mexico, 200 College Road, Gallup, NM 87301, USA;Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
Venue:
Information Sciences: an International Journal
Year:
2011

Citing 15
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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.