The performance of the traditional space-time adaptive processing (STAP) methods will deteriorate in the presence of both mainlobe deceptive jammers and clutter. In order to mitigate the clutter and deceptive jammers simultaneously, a 3-D atomic norm minimization (3-D-ANM) STAP method based on subspace projection is proposed with an element pulse coding-multiple-input multiple-output radar. At the modeling stage, by coding both the transmit elements and slow-time pulses, the echoes corresponding to different range ambiguity regions can be discriminated via equivalent spatial beampattern directions. In this regard, the target, mainlobe deceptive jammers, and range-ambiguous clutter can be distinguished in transmit–receive-Doppler domain by exploiting the distribution differences. Subsequently, the STAP method is utilized to mitigate clutter and deceptive jammers jointly. In specific, the subspace projection technique is adopted to ensure the comprehensiveness of subsequent covariance matrices estimation. Moreover, two 3-D-ANM problems are formulated to recover the jammer-plus-range-ambiguous clutter covariance and range-unambiguous clutter covariance matrices, respectively. Particularly, in order to reduce the computational complexity, the constructed 3-D-ANM problems are solved via alternating direction method of multipliers (ADMM). In addition, theoretical derivations and proofs on the properties of the developed methods are given. A series of simulations verify the efficacy of the proposed 3-D-ANM STAP algorithm in mitigating deceptive jammers and clutter simultaneously.


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    Title :

    Joint Deceptive Jammer and Clutter Suppression via 3-D-ANM With EPC-MIMO Radar


    Contributors:
    Gao, Jie (author) / Zhu, Shengqi (author) / Lan, Lan (author) / Sui, Jinxin (author) / Li, Ximin (author) / Liao, Guisheng (author)


    Publication date :

    2025-04-01


    Size :

    3557632 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English