Volume 4 Issue 6
Dec.  2015
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Article Navigation >  Journal of Radars  > 2015  >  4(6): 674-680
WANG Yuqi, SUN Guangcai, YANG Jun, et al. Passive localization algorithm for radiation source based on long synthetic aperture[J]. Journal of Radars, 2020, 9(1): 185–194. doi: 10.12000/JR19080
Citation: HAN Ping, WANG Huan. Synthetic Aperture Radar Target Feature Extraction and Recognition Based on Improved Sparsity Preserving Projections[J]. Journal of Radars, 2015, 4(6): 674-680. doi: 10.12000/JR15068
shu

Synthetic Aperture Radar Target Feature Extraction and Recognition Based on Improved Sparsity Preserving Projections

DOI: 10.12000/JR15068 CSTR: 32380.14.JR15068
  • 1.

    (Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China)

  • 2.

    (Jiujiang Airport Branch, Jiangxi Airports Group Company, Jiujiang 332000, China)

Funds:

The National Natural Science Foundation of China (61571442, 61471365), The State Key Program of National Natural Science Foundation of China (61231017), The Fundamental Research Funds for the Central Universities (3122014C004)

  • Received Date: 2015-05-28
  • Rev Recd Date: 2015-09-16
  • Publish Date: 2015-12-28
    Abstract
  • We have proposed an improved Sparsity Preserving Projection (SPP) method to implement target feature extraction. It combines the SPP feature extraction using the idea of the Locality Preserving Projection (LPP) scheme to build a new objective function, which can not only maintain the relationship of sparse reconstruction between the samples but also minimize the distance between similar sample types in the projection space. Experimental results with Moving and Stationary Target Acquisition and Recognition (MSTAR) Synthetic Aperture Radar (SAR) data sets show that the average recognition rate using the proposed method is up to 97.81% without knowing the target to be azimuth, which can improve the target recognition result even further for obvious reasons. The proposed method is an effective one for SAR target recognition.

     

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    • References(15)
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