期刊
INFORMATION SCIENCES
卷 181, 期 10, 页码 1818-1835出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.10.025
关键词
Information fusion; Belief function; Complexity reduction; Robot perception; DSmT; Measure of similarity; Distance; Lattice
资金
- National Natural Science Foundation of China [60804063, 60805032]
- Natural Science Foundation of Jiangsu Province [BK2010403]
- Public Funds of Image Processing and Intelligent Control Key Laboratory of Chinese Education Ministry [200902]
- Science and Technology Innovation Foundation in Southeast University [3208000501]
- Aeronautical Science Foundation of China [20100169001]
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 (GEM). 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. (C) 2010 Elsevier Inc. All rights reserved.
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