J4 ›› 2015, Vol. 42 ›› Issue (6): 43-48.doi: 10.3969/j.issn.1001-2400.2015.06.008

• 研究论文 • 上一篇    下一篇

窄带雷达观测下的锥体目标参数估计方法

  韩勋;杜兰;刘宏伟   

  1. (西安电子科技大学 雷达信号处理国家重点实验室,陕西 西安  710071)
  • 收稿日期:2014-06-16 出版日期:2015-12-20 发布日期:2016-01-25
  • 通讯作者: 韩勋
  • 作者简介:韩勋(1990-),男,西安电子科技大学博士研究生,E-mail: andyhanxun@126.com.
  • 基金资助:

    国家自然科学基金资助项目(61271024,61201296,61322103);全国优秀博士学位论文作者专项资金资助项目(FANEDD-201156)

Parameter estimation method for the cone-shaped target under narrow-band radar observation

HAN Xun;DU Lan;LIU Hongwei   

  1. (National Key Lab. of Radar Signal Processing, Xidian Univ., Xi'an  710071, China)
  • Received:2014-06-16 Online:2015-12-20 Published:2016-01-25
  • Contact: HAN Xun

摘要:

针对现有的基于进动特征的空间锥体目标参数估计假设散射中心微多普勒频率为正弦与实际情况不符的问题,提出了一种利用窄带回波中所包含散射中心微多普勒频率进行参数估计的方法.在建立目标进动模型的基础上,推导了顶部和底部散射中心理论微多普勒频率,然后对底部散射中心微多普勒进行展开,并分析了展开系数和目标尺寸与进动参数之间的关系,最后结合顶部与底部散射中心微多普勒建立线性方程组对展开系数进行求解,根据所得展开系数计算了目标尺寸与进动参数.

关键词: 目标识别, 锥体目标, 微多普勒频率, 参数估计, 特征提取

Abstract:

The parameter estimation for the space cone-shaped target based on precession is very important for target discrimination. This paper proposes a novel parameter estimation method via the scattering centers' micro-Doppler frequency contained in the narrowband echo. After the establishment of the target's precession model, the theoretical variation of top and bottom scattering centers' micro-Doppler frequency are derived, and then the micro-Doppler frequency of the bottom scattering center is expanded, and the relationship between the expansion coefficients and the target's size and precession parameter is analyzed. Finally, a linear system of equations is established to solve the expansion coefficients with the top and bottom scattering centers' micro-Doppler frequencies, and then the target's size and precession parameters are calculated based on the coefficients. Experiments based on electromagnetic computation data indicate that the proposed method is valid and accurate.

Key words: target recognition, cone-shaped target, micro-Doppler frequency, parameter estimation, feature extraction

中图分类号: 

  • TN957.52