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CUI Linfeng, WU Min, HAO Chengpeng, et al. Fast and gridless sparse recovery STAP method based on nonconvex relaxation of atomic norm[J]. Journal of Radars, 2025, 14(6): 1376–1392. doi: 10.12000/JR25125
Citation: CUI Linfeng, WU Min, HAO Chengpeng, et al. Fast and gridless sparse recovery STAP method based on nonconvex relaxation of atomic norm[J]. Journal of Radars, 2025, 14(6): 1376–1392. doi: 10.12000/JR25125
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Fast and Gridless Sparse Recovery STAP Method Based on Nonconvex Relaxation of Atomic Norm

DOI: 10.12000/JR25125 CSTR: 32380.14.JR25125
  • 1.

    Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    State Key Laboratory of Acoustics and Marine Information, Beijing 100049, China

Funds:  The National Natural Science Foundation of China (62371446, 62471463, 62001468), Youth Innovation Promotion Association, CAS (2023030)
More Information
  • Corresponding author: HAO Chengpeng, haochengp@mail.ioa.ac.cn
  • Received Date: 2025-07-18
  • Rev Recd Date: 2025-11-14
    • Available Online: 2025-11-16
    Abstract
  • Sparse Recovery-based Space-Time Adaptive Processing (SR-STAP) methods offer significant advantages in nonhomogeneous clutter environments owing to their minimal requirement for training samples. However, the performance of most existing SR-STAP methods is limited by the grid mismatch effect, which arises from the discretization of the space-time plane. To address this problem and enhance clutter suppression, this paper proposes a gridless SR-STAP method based on a nonconvex relaxation of atomic norm. The proposed method formulates a gridless sparse recovery model using atoms in the continuous domain, overcoming the grid mismatch effect inherent in traditional discrete dictionary-based methods. Furthermore, a nonconvex relaxation of atomic norm is employed, with optimization progress iteratively executed using a reweighting strategy, which effectively surpasses the resolution limit. In addition, to address the high computational complexity associated with solving semidefinite programming, a fast solution scheme based on an improved Alternating Direction Method of Multipliers (ADMM) is proposed. This scheme exploits the low-rank and block-Toeplitz properties of the clutter covariance matrix, reduces the algorithm’s complexity using an approximate positive semidefinite projection technique, and accelerates convergence with an adaptive penalty parameter based on hypergradient descent. Simulation results and real-measured data demonstrate that the proposed method achieves superior clutter suppression, robust target detection performance, and higher computational efficiency compared to existing SR-STAP methods.

     

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