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Title: Decision Level Fusion: An Event Driven Approach

Abstract

This study presents a technique that combines the occurrence of certain events, as observed by different sensors, in order to detect and classify objects. This technique explores the extent of dependence between features being observed by the sensors, and generates more informed probability distributions over the events. Provided some additional information about the features of the object, this fusion technique can outperform other existing decision level fusion approaches that may not take into account the relationship between different features.

Authors:
 [1];  [2];  [1];  [1]
  1. North Carolina State Univ., Raleigh, NC (United States)
  2. Univ. of Minnesota, Minneapolis, MN (United States)
Publication Date:
Research Org.:
North Carolina State Univ., Raleigh, NC (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
OSTI Identifier:
1452682
DOE Contract Number:  
NA0002576
Resource Type:
Conference
Resource Relation:
Conference: 26. European Signal Processing Conference, Rome (Italy), 3-7 Sep 2018
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Sensor Fusion; Decision Level Fusion; Event based Classification; Coupling

Citation Formats

Roheda, Siddharth, Luo, Zhi -Quan, Krim, Hamid, and Wu, Tianfu. Decision Level Fusion: An Event Driven Approach. United States: N. p., 2018. Web. doi:10.23919/EUSIPCO.2018.8553412.
Roheda, Siddharth, Luo, Zhi -Quan, Krim, Hamid, & Wu, Tianfu. Decision Level Fusion: An Event Driven Approach. United States. doi:10.23919/EUSIPCO.2018.8553412.
Roheda, Siddharth, Luo, Zhi -Quan, Krim, Hamid, and Wu, Tianfu. Mon . "Decision Level Fusion: An Event Driven Approach". United States. doi:10.23919/EUSIPCO.2018.8553412. https://www.osti.gov/servlets/purl/1452682.
@article{osti_1452682,
title = {Decision Level Fusion: An Event Driven Approach},
author = {Roheda, Siddharth and Luo, Zhi -Quan and Krim, Hamid and Wu, Tianfu},
abstractNote = {This study presents a technique that combines the occurrence of certain events, as observed by different sensors, in order to detect and classify objects. This technique explores the extent of dependence between features being observed by the sensors, and generates more informed probability distributions over the events. Provided some additional information about the features of the object, this fusion technique can outperform other existing decision level fusion approaches that may not take into account the relationship between different features.},
doi = {10.23919/EUSIPCO.2018.8553412},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {1}
}

Conference:
Other availability
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