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

Research Article

A Novel Method of Flight Target Altitude Attributes Identification for HFSWR

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  • @INPROCEEDINGS{10.1007/978-3-319-73447-7_39,
        author={Shuai Shao and Changjun Yu and Kongrui Zhao},
        title={A Novel Method of Flight Target Altitude Attributes Identification for HFSWR},
        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 Altitude attribute identification Propagation attenuation},
        doi={10.1007/978-3-319-73447-7_39}
    }
    
  • Shuai Shao
    Changjun Yu
    Kongrui Zhao
    Year: 2018
    A Novel Method of Flight Target Altitude Attributes Identification for HFSWR
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73447-7_39
Shuai Shao1, Changjun Yu1,*, Kongrui Zhao1
  • 1: Harbin Institute of Technology
*Contact email: yuchangjun@hit.edu.cn

Abstract

Recently, flight target altitude estimation using high frequency surface wave radar (HFSWR) gains popularity. In practical flight target early warning applications, the most concerned characteristics of the targets are generally the high/low altitude attribute and the line-of-sight/over-the-horizon. For HFSWR, the target altitude attribute identification is somewhat more meaningful and available than the accurate altitude estimation. In this paper, a novel method, which is based on the propagation attenuation of the vertically polarized wave at different altitude intervals, is proposed to identify target altitude attribute in HFSWR. The method continuously identify the target altitude attributes and evaluate the credibility of altitude attributes identification. Practical trials demonstrate that the flight target altitude attribute is quickly identified using a small amount of data, and meanwhile the credibility is superior to 0.9.