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A three-way incremental-learning algorithm for radar emitter identification

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Abstract

Radar emitter identification has been recognized as an indispensable task for electronic intelligence system. With the increasingly accumulated radar emitter intelligence and information, one key issue is to rebuild the radar emitter classifier efficiently with the newly-arrived information. Although existing incremental learning algorithms are superior in saving significant computational cost by incremental learning on continuously increasing training samples, they are not adaptable enough yet when emitter types, features and samples are increasing dramatically. For instance, the intra-pulse characters of emitter signals could be further extracted and thus expand the feature dimension. The same goes for the radar emitter type dimension when samples from new radar emitter types are gathered. In addition, existing incremental classifiers are still problematic in terms of computational cost, sensitivity to data input order, and difficulty in multiemitter type identification. To address the above problems, we bring forward a three-way incremental learning algorithm (TILA) for radar emitter identification which is adaptable for the increase in emitter features, types and samples.

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Author information

Authors and Affiliations

  1. Science and Technology on Information System Engineering Laboratory, Nanjing Research Institute of Electronic Engineering (NRIEE), Nanjing, 210007, China

    Xin Xu & Jianhong Wang

  2. State Key Laboratory for Novel Software and Technology, Nanjing University, Nanjing, 210093, China

    Wei Wang

Authors
  1. Xin Xu
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  2. Wei Wang
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  3. Jianhong Wang
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Corresponding author

Correspondence to Xin Xu.

Additional information

Xin Xu received her PhD in School of Computing from National University of Singapore, Singapore in 2006. She is currently a Senior Research Engineer in Science and Technology on Information System Engineering Laboratory, China. Her research interests are in the area of data mining and data fusion.

Wei Wang received his PhD in electrical and computer engineering from National University of Singapore, Singapore in 2008. He is currently an associated professor in Department of Computer Science and Technology, Nanjing University, China. His research interests are in the area of wireless sensor networks and data fusion.

Jianhong Wang received his PhD in College of Automation Engineer from Nanjing University of Aeronautics and Astronautics, China. He is currently an associate professor in Jingdezhen Ceramic Institute, China. His research interests include realtime distributed control, optimization and system identification.

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Xu, X., Wang, W. & Wang, J. A three-way incremental-learning algorithm for radar emitter identification. Front. Comput. Sci. 10, 673–688 (2016). https://doi.org/10.1007/s11704-015-4457-7

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  • DOI: https://doi.org/10.1007/s11704-015-4457-7

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