کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
394765 665841 2011 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Evidence supporting measure of similarity for reducing the complexity in information fusion
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Evidence supporting measure of similarity for reducing the complexity in information fusion
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 181, Issue 10, 15 May 2011, Pages 1818–1835
نویسندگان
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