Volume 10 Issue 6
Dec.  2021
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Article Navigation >  Journal of Radars  > 2021  >  10(6): 919-928
CUI Ning, XING Kun, DUAN Keqing, et al. Fast tensor-based three-dimensional sparse Bayesian learning space-time adaptive processing method[J]. Journal of Radars, 2021, 10(6): 919–928. doi: 10.12000/JR21140
Citation: CUI Ning, XING Kun, DUAN Keqing, et al. Fast tensor-based three-dimensional sparse Bayesian learning space-time adaptive processing method[J]. Journal of Radars, 2021, 10(6): 919–928. doi: 10.12000/JR21140
shu

Fast Tensor-based Three-dimensional Sparse Bayesian Learning Space-Time Adaptive Processing Method

DOI: 10.12000/JR21140
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • 2.

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    School of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510006, China

Funds:  The National Natural Science Foundation of China (61871397)
More Information
  • Corresponding author: DUAN Keqing, duankeqing@aliyun.com; YU Zhongjun, yuzj@ucas.ac.cn
  • Received Date: 2021-09-26
  • Accepted Date: 2021-12-06
  • Rev Recd Date: 2021-12-03
    • Available Online: 2021-12-07
  • Publish Date: 2021-12-23
    Abstract
  • When airborne radar is applied to the non-side-looking mode, moving target detection performance considerably degrades because of the nonstationary clutter. Conventional three-dimensional (3D) Space-Time Adaptive Processing (STAP) can effectively eliminate the nonstationary clutter via adaptively constructing an elevation-azimuth-Doppler 3D filter. However, large system degrees of freedom lead to a shortage of training samples in a heterogeneous environment. Although introducing the Sparse Recovery (SR) technology substantially reduces the sample requirement, the practical application of this technology is limited by computational complexities. To solve the above problems, this paper proposes a fast 3D sparse Bayesian learning STAP, based on the third-order tensor structure of echo data. In the proposed method, large-scale matrix calculation is decomposed into small-scale matrix calculation using a low-complexity tensor-based operation, thus considerably reducing the computational load. Exhaustive numerical experiments verify that the proposed method directly reduces the computational load by several orders of magnitude compared with that of the existing SR-STAP algorithms, while maintaining the SR-STAP performance. Therefore, the tensor-based method is a superior processing method than the vector-based method in engineering.

     

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