An Online Multisensor Data Fusion Framework for Radar Emitter Classification

Radar emitter classification is a special application of data clustering for classifying unknown radar emitters in airborne electronic support system. In this paper, a novel online multisensor data fusion framework is proposed for radar emitter classification under the background of network centric...

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Main Authors: Dongqing Zhou, Xing Wang, Siyi Cheng, Xi Zhang
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:International Journal of Aerospace Engineering
Online Access:http://dx.doi.org/10.1155/2016/5372510
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author Dongqing Zhou
Xing Wang
Siyi Cheng
Xi Zhang
author_facet Dongqing Zhou
Xing Wang
Siyi Cheng
Xi Zhang
author_sort Dongqing Zhou
collection DOAJ
description Radar emitter classification is a special application of data clustering for classifying unknown radar emitters in airborne electronic support system. In this paper, a novel online multisensor data fusion framework is proposed for radar emitter classification under the background of network centric warfare. The framework is composed of local processing and multisensor fusion processing, from which the rough and precise classification results are obtained, respectively. What is more, the proposed algorithm does not need prior knowledge and training process; it can dynamically update the number of the clusters and the cluster centers when new pulses arrive. At last, the experimental results show that the proposed framework is an efficacious way to solve radar emitter classification problem in networked warfare.
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institution Kabale University
issn 1687-5966
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language English
publishDate 2016-01-01
publisher Wiley
record_format Article
series International Journal of Aerospace Engineering
spelling doaj-art-caad86db476b49efb0a10e04e7dafc122025-02-03T06:08:36ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742016-01-01201610.1155/2016/53725105372510An Online Multisensor Data Fusion Framework for Radar Emitter ClassificationDongqing Zhou0Xing Wang1Siyi Cheng2Xi Zhang3Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaAeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, ChinaRadar emitter classification is a special application of data clustering for classifying unknown radar emitters in airborne electronic support system. In this paper, a novel online multisensor data fusion framework is proposed for radar emitter classification under the background of network centric warfare. The framework is composed of local processing and multisensor fusion processing, from which the rough and precise classification results are obtained, respectively. What is more, the proposed algorithm does not need prior knowledge and training process; it can dynamically update the number of the clusters and the cluster centers when new pulses arrive. At last, the experimental results show that the proposed framework is an efficacious way to solve radar emitter classification problem in networked warfare.http://dx.doi.org/10.1155/2016/5372510
spellingShingle Dongqing Zhou
Xing Wang
Siyi Cheng
Xi Zhang
An Online Multisensor Data Fusion Framework for Radar Emitter Classification
International Journal of Aerospace Engineering
title An Online Multisensor Data Fusion Framework for Radar Emitter Classification
title_full An Online Multisensor Data Fusion Framework for Radar Emitter Classification
title_fullStr An Online Multisensor Data Fusion Framework for Radar Emitter Classification
title_full_unstemmed An Online Multisensor Data Fusion Framework for Radar Emitter Classification
title_short An Online Multisensor Data Fusion Framework for Radar Emitter Classification
title_sort online multisensor data fusion framework for radar emitter classification
url http://dx.doi.org/10.1155/2016/5372510
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