The effective suppression of mainlobe deceptive jamming targets ensures that radar systems accurately and reliably detect and track true targets in complex electromagnetic environments. For the self-screening jamming scenario, this study proposes a method based on the frequency-diverse array (FDA) multiple-input–multiple-output (MIMO) radar to suppress mainlobe deceptive jamming targets effectively, which consists of a series of steps. In the warning stage, a random logarithmic frequency offset is designed to obtain the unambiguous range and angle parameters of targets, and it is proved that under this offset, the range parameter in the receiver beampattern of FDA-MIMO radar is unambiguous. In the jamming rejection stage, an ($M\times 1$)-dimensional unambiguous range compensation vector is constructed based on the random logarithmic frequency offset to suppress the jamming targets generated by self-protection jammers. By compensating the transmission steering vector coefficients of targets, the true, mainlobe deceptive, and sidelobe angle-deceptive jamming targets can be distinguished on the combined transmission–reception spatial frequency domain. Finally, to separate the true target signal from the jamming signal at the receiver end, an adaptive filter is designed. Due to the proposed compensation process, the adaptive filter only needs to match the true target and suppress angle-deceptive jamming targets, without the need to suppress mainlobe deceptive jamming targets by setting null steering regions. In addition, a singular spectrum analysis algorithm is used to select samples to estimate the exact covariance matrix of the interference plus noise. Numerical simulations verify that the proposed method has better robustness and effectiveness than the existing methods in suppressing mainlobe deceptive jamming targets.


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    Title :

    Suppress Mainlobe Deceptive Jamming Target Under Unambiguous Range Compensation Based on FDA-MIMO Radar


    Contributors:
    Yuan, Tao (author) / He, Feng (author) / Dong, Zhen (author) / Su, Yi (author) / Fan, Zhuoya (author) / Yu, Lei (author)


    Publication date :

    2024-10-01


    Size :

    3551897 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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