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SUN Jiarui, HAO Chengpeng, SUN Mengru, et al. An anti-jamming detection method based on expectation maximization classification[J]. Journal of Radars, in press. doi: 10.11999/JR25145
Citation: SUN Jiarui, HAO Chengpeng, SUN Mengru, et al. An anti-jamming detection method based on expectation maximization classification[J]. Journal of Radars, in press. doi: 10.11999/JR25145
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An Anti-jamming Detection Method Based On Expectation Maximization Classification

DOI: 10.11999/JR25145 CSTR: 32380.14.JR25145
  • 1.

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

  • 2.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • 3.

    Qingdao Branch, Institute of Acoustics, Chinese Academy of Sciences, Qingdao 266114, China

Funds:  The National Natural Science Foundation of China Young Scientist Program (62201564, 62301552), The National Natural Science Foundation of China General Program(62571525), Youth Innovation Promotion Association CAS (2023028)
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    Abstract
  • In complex environments with active artificial jammers, the accuracy of signal parameter estimation typically deteriorates significantly, consequently degrading target detection performance. To effectively address the challenge, this paper proposes an anti-jamming detection framework that leverages expectation-maximization classification. Specifically, we propose a Noise-Covered Pulse (NCP) detection method in passive detection mode, a passive jamming early warning mechanism is established: a latent variable model representing NCP categories is constructed. By combining the Expectation Maximization algorithm with matrix decomposition, NCP sample classification and angle/energy parameter estimation are achieved, enabling robust adaptive NCP detection. In radar active mode, we propose a Coherent Jamming (CJ) target detection method in active detection mode, establish an active anti-jamming detection architecture: Construct a classification model based on the presence hypothesis of target echoes and CJ. Utilize grid search and the Expectation-Maximization algorithm to perform sample classification and angle estimation, enabling CJ identification and adaptive target detection. Simulation results show that the proposed method can effectively identify the range bin within the target or jammer, accurately estimate the incidence angle of the target and jammer, and improve the anti-jamming performance of constant false alarm target detection.

     

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