Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II

Research Article

Estimating of RCS of Ionosphere for High Frequency Surface Wave Radar

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  • @INPROCEEDINGS{10.1007/978-3-319-73447-7_27,
        author={Yang Xuguang and Yu Changjun and Liu Aijun and Wang Linwei},
        title={Estimating of RCS of Ionosphere for High Frequency Surface Wave Radar},
        proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II},
        proceedings_a={MLICOM},
        year={2018},
        month={2},
        keywords={High Frequency Surface Wave Radar Ionosphere RCS},
        doi={10.1007/978-3-319-73447-7_27}
    }
    
  • Yang Xuguang
    Yu Changjun
    Liu Aijun
    Wang Linwei
    Year: 2018
    Estimating of RCS of Ionosphere for High Frequency Surface Wave Radar
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73447-7_27
Yang Xuguang, Yu Changjun1,*, Liu Aijun1, Wang Linwei1
  • 1: Harbin Institute of Technology
*Contact email: Yuchangjun@hit.edu.cn

Abstract

High Frequency Surface Wave Radar (HFSWR) has been shown to provide enhanced performance in over the horizon detection of targets and sea states remote sensing by the returns of targets and ocean surface. Meanwhile, HFSWR can also receive ionospheric echoes reflected by the ionosphere, which severely affect the radar detection performance. In this paper, the radar cross section (RCS) of ionosphere for HFSWR is estimated, which would help quantify the impact of the ionosphere to radar system and the performance of clutter mitigation techniques. Simulations are provided to illustrate the effect of parameters including radar operating frequency, scale size of irregularities, aspect angle and detection range on the RCS of ionosphere.