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

Research Article

Parameters Estimation of Precession Cone Target Based on Micro-Doppler Spectrum

Download
176 downloads
  • @INPROCEEDINGS{10.1007/978-3-319-73447-7_55,
        author={MingFeng Wang and AiJun Liu and LinWei Wang and ChangJun Yu},
        title={Parameters Estimation of Precession Cone Target Based on Micro-Doppler Spectrum},
        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={Procession cone target micro-Doppler features Search method Precession parameters estimation},
        doi={10.1007/978-3-319-73447-7_55}
    }
    
  • MingFeng Wang
    AiJun Liu
    LinWei Wang
    ChangJun Yu
    Year: 2018
    Parameters Estimation of Precession Cone Target Based on Micro-Doppler Spectrum
    MLICOM
    Springer
    DOI: 10.1007/978-3-319-73447-7_55
MingFeng Wang1, AiJun Liu1,*, LinWei Wang1, ChangJun Yu1
  • 1: Harbin Institute of Technology
*Contact email: mylaj@hitwh.edu.cn

Abstract

The micro-Doppler (m-D) provides valuable information for the motion parameters extraction and the target recognition of space targets. To address the issue of estimating the motion parameters of precession warhead targets, a new method based on the m-D spectrum of the top and the bottom of the cone is proposed in this paper. In this method, the m-D features of the cone target are firstly extracted by calculating the first-order moments of the time-frequency distribution of the echo signal. Then, the motion parameters of the target are roughly estimated by the Fourier transformation of the m-D curve. Based on the rough estimation, the search method is employed to estimate the motion parameters of the cone target precisely. The validity of the proposed method is verified by the analysis data.