In the field of radar target recognition, open-set recognition can be used to solve noncooperative target recognition. The main difficulty of open-set recognition is finding a closed classification boundary to distinguish the known and unknown targets simultaneously. This article proposes an open-set recognition method that trains a neural network through a distance-based loss function and combines the OpenMax classifier, which solves the open-set recognition problem of finding the closed boundary. With this method, the known and unknown classes can be effectively in various sample sets identified without relying on a prior threshold to assist in searching boundaries. In addition, simulation results show that the rejection accuracy exceeds 95% for eight types of autonomuos aerial vehicle (AAV) targets based on high-resolution range profile, which indicates excellent performance for open-set recognition.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Threshold Insensitive Open-Set Recognition Scheme for AAV Targets Based on HRRP


    Contributors:
    Tao, Shifei (author) / Mei, Mingfei (author) / Luo, Jia (author) / Yan, Lingjie (author) / Huang, Xin (author)


    Publication date :

    2025-04-01


    Size :

    3684075 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    HRRPSeqNet: Open-Set Recognition of Space Target Motions Using HRRP Sequences

    Zhang, Yanjin / Feng, Xuejian / Yin, Hongcheng et al. | IEEE | 2025


    Target Recognition of Radar HRRP Using the Envelope Reconstruction

    Zhang, Pengfei / Chan, Li / Zhou, Hongxi et al. | British Library Conference Proceedings | 2018


    Target Recognition of Radar HRRP Using the Envelope Reconstruction

    Zhang, Pengfei / Chan, Li / Zhou, Hongxi et al. | Springer Verlag | 2017


    Radar HRRP Target Recognition Based on Hybrid Quantum Neural Networks

    Liu, Xin / Zhou, Daiying / Huang, Qiuyan | IEEE | 2025