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International Journal of Aerospace Engineering
Volume 2016, Article ID 5372510, 16 pages
Research Article

An Online Multisensor Data Fusion Framework for Radar Emitter Classification

Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi’an, Shaanxi 710038, China

Received 1 October 2015; Accepted 3 April 2016

Academic Editor: Hikmat Asadov

Copyright © 2016 Dongqing Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.