Volume 14 Issue 2
Apr.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(2): 280-292
LAN Xiaoyu, HU Jiyan, LIANG Mingshen, et al. Sparse DOA estimation method based on Riemann averaging under strong intermittent jamming[J]. Journal of Radars, 2025, 14(2): 280–292. doi: 10.12000/JR24175
Citation: LAN Xiaoyu, HU Jiyan, LIANG Mingshen, et al. Sparse DOA estimation method based on Riemann averaging under strong intermittent jamming[J]. Journal of Radars, 2025, 14(2): 280–292. doi: 10.12000/JR24175
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Sparse DOA Estimation Method Based on Riemann Averaging under Strong Intermittent Jamming

DOI: 10.12000/JR24175 CSTR: 32380.14.JR24175
  • 1.

    College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang 110136, China

  • 2.

    Liaoning Provincial Key Laboratory of Aerospace Information Perception and Intelligent Processing, Shenyang 110136, China

Funds:  National Science Foundation for Young Scientists of China (61801308), Aeronautical Science Foundation (2020Z017054001), General Program of the Education Department of Liaoning Province (LJKMZ20220535), Natural Science Foundation of Liaoning Province of China (2024-MS-135)
More Information
  • Corresponding author: HU Jiyan, 1534018950@qq.com
  • Received Date: 2024-08-31
  • Rev Recd Date: 2024-11-08
    • Available Online: 2024-11-14
  • Publish Date: 2024-12-11
    Abstract
  • Aiming to address the problem of increased radar jamming in complex electromagnetic environments and the difficulty of accurately estimating the target signal close to a strong jamming signal, this paper proposes a sparse Direction of Arrival (DOA) estimation method based on Riemann averaging under strong intermittent jamming. First, under the extended coprime array data model, the Riemann averaging is introduced to suppress the jamming signal by leveraging the property that the target signal is continuously active while the strong jamming signal is intermittently active. Then, the covariance matrix of the processed data is vectorized to obtain virtual array reception data. Finally, the sparse iterative covariance-based estimation method, which is used for estimating the DOA under strong intermittent interference, is employed in the virtual domain to reconstruct the sparse signal and estimate the DOA of the target signal. The simulation results show that the method can provide highly accurate DOA estimation for weak target signals whose angles are closely adjacent to strong interference signals when the number of signal sources is unknown. Compared with existing subspace algorithms and sparse reconstruction class algorithms, the proposed algorithm has higher estimation accuracy and angular resolution at a smaller number of snapshots, as well as a lower signal-to-noise ratio.

     

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