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Feature Extraction of Radar Emitter Signal Based on Empirical Mode Decomposition

  • Conference paper
Information Engineering and Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 154))

Abstract

To enhance accurate recognition rate of radar emitter signal (RES), a novel feature extraction method of radar emitter signal is proposed based on empirical mode decomposition (EMD) theory. The EMD algorithm is used to decompose the radar emitter signal into a number of intrinsic mode functions (IMF) and a residue component, these IMFs can reflect characteristics of the radar emitter signal. After that, the energy of each IMF is calculated and normalized, which is regarded as an element of the feature vector. Finally, it realizes the recognition of radar emitter signal through BP neural networks (BPNN). Experiment results shows that EMD-based feature extraction method of radar emitter signal is an effective method, energy feature that extraction from EMD decomposition has a higher recognition rate. The main work of this paper is that it applied the EMD method to feature extraction of radar emitter signal for the first time.

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References

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    ZHU Bin & JIN Wei-dong

Authors
  1. ZHU Bin
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  2. JIN Wei-dong
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Corresponding author

Correspondence to ZHU Bin .

Editor information

Editors and Affiliations

  1. South-Central University for Nationaliti, Wuhan, 430074, China, People's Republic

    Rongbo Zhu

  2. , School of Computer and Information, Chongqing Normal University, Chongqing, 401331, China, People's Republic

    Yan Ma

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© 2012 Springer-Verlag London Limited

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Bin, Z., Wei-dong, J. (2012). Feature Extraction of Radar Emitter Signal Based on Empirical Mode Decomposition. In: Zhu, R., Ma, Y. (eds) Information Engineering and Applications. Lecture Notes in Electrical Engineering, vol 154. Springer, London. https://doi.org/10.1007/978-1-4471-2386-6_93

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  • DOI: https://doi.org/10.1007/978-1-4471-2386-6_93

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-2385-9

  • Online ISBN: 978-1-4471-2386-6

  • eBook Packages: EngineeringEngineering (R0)

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