This article investigates the problem of specific emitter identification (SEI), i.e., radar emitter fingerprint or individual emitter identification, which first measures the emitter-specific differences caused by radar's nonlinearities, e.g., mixer, power amplifiers, transmitter, and then makes a decision. In this article, the SEI problem is considered in the single-modal, dual-modal, and multimodal scenarios, respectively. First, a multimodal subspace interactive mutual unit is proposed to perform information interaction between radar signal and its multiple transformations. Based on this, a data-driven multimodal subspace interactive mutual network is then built to solve the SEI problem. Extensive experiment results demonstrate that the proposed algorithm achieves superior identification performance on the airplane measured data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Multimodal Subspace Interactive Mutual Network for Specific Emitter Identification


    Contributors:
    Zhu, Zhigang (author) / Ji, Hongbing (author) / Li, Lin (author)


    Publication date :

    2023-08-01


    Size :

    1988013 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    A Survey of Specific Emitter Identification

    Li, Y. / Qin, X. | TIBKAT | 2023


    Adaptive Signal Feature-Based Deep Learning for Enhanced Specific Emitter Identification

    Xu, Junzhi / Chen, Miao / Wen, Fangqing et al. | IEEE | 2024


    Universal Black-Box Adversarial Attack on Deep Learning for Specific Emitter Identification

    Chen, Kailun / Zhang, Yibin / Cai, Zhenxin et al. | IEEE | 2024