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An Online Multisensor Data Fusion Framework for Radar Emitter Classification
Joint Authors
Zhou, Dongqing
Wang, Xing
Cheng, Siyi
Zhang, Xi
Source
International Journal of Aerospace Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-05-30
Country of Publication
Egypt
No. of Pages
16
Abstract EN
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.
American Psychological Association (APA)
Zhou, Dongqing& Wang, Xing& Cheng, Siyi& Zhang, Xi. 2016. An Online Multisensor Data Fusion Framework for Radar Emitter Classification. International Journal of Aerospace Engineering،Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1105005
Modern Language Association (MLA)
Zhou, Dongqing…[et al.]. An Online Multisensor Data Fusion Framework for Radar Emitter Classification. International Journal of Aerospace Engineering No. 2016 (2016), pp.1-16.
https://search.emarefa.net/detail/BIM-1105005
American Medical Association (AMA)
Zhou, Dongqing& Wang, Xing& Cheng, Siyi& Zhang, Xi. An Online Multisensor Data Fusion Framework for Radar Emitter Classification. International Journal of Aerospace Engineering. 2016. Vol. 2016, no. 2016, pp.1-16.
https://search.emarefa.net/detail/BIM-1105005
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1105005