Volume 8 Issue 1
Mar.  2019
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Article Navigation >  Journal of Radars  > 2019  >  8(1): 44-53
LI Chunsheng, YU Ze, and CHEN Jie. Overview of techniques for improving high-resolution spaceborne SAR imaging and image quality[J]. Journal of Radars, 2019, 8(6): 717–731. doi: 10.12000/JR19085
Citation: YUAN Ba, YAO Ping, and ZHENG Tianyao. Radar emitter signal identification based on weighted normalized singular-value decomposition[J]. Journal of Radars, 2019, 8(1): 44–53. doi: 10.12000/JR18053
shu

Radar Emitter Signal Identification Based on Weighted Normalized Singular-value Decomposition

DOI: 10.12000/JR18053
  • ①.

    Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

  • ②.

    University of Chinese Academy of Sciences, Beijing 100049, China

Funds:  The Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19020400)
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  • Corresponding author: YUAN Ba, yuanba@ict.ac.cn
  • Received Date: 2018-07-09
  • Rev Recd Date: 2018-08-28
    • Available Online: 2018-10-08
  • Publish Date: 2019-02-28
    Abstract
  • With the continuous advancement of modern technology, more types of radar and related technologies are continuously being developed, and the identification of radar emitter signals has gradually become a very important research field. This paper focuses on the identification of modulation types in radar emitter signal identification. We propose a weighted normalized Singular-Value Decomposition (SVD) feature extraction algorithm, which is based on the perspective of data energy and SVD. The filtering effect of complex SVD is analyzed, as well as the influence of the number of rows of data matrix on the decomposition results, and the recognition effect of different classification models. The experimental results show that the algorithm has better filtering and recognition effects on common radar signals. Under –20 dB, the cosine similarity values of the reconstructed and original signals remain at about 0.94, and the recognition accuracy remains above 97% under a confidence level α of 0.65. In addition, experiments show that the weighted normalized SVD feature extraction algorithm has better robustness than the traditional Principal Component Analysis (PCA) algorithm.

     

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    • References(22)
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