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基于RPRG+ICCD与RANSAC的多分量多项式相位信号参数估计
引用本文:程永亮,邵杰,赵一鹤.基于RPRG+ICCD与RANSAC的多分量多项式相位信号参数估计[J].电子科技,2020,33(2):66-70.
作者姓名:程永亮  邵杰  赵一鹤
作者单位:南京航空航天大学 电子信息工程学院,江苏 南京211106
基金项目:教育部重点实验室开放研究基金(UASP1604)
摘    要:多分量多项式相位信号的检测和参数估计是多项式相位信号处理的一大难点。时频分析方法能够通过时频分布表征多项式相位信号的瞬时频率,通过对提取的瞬时频率进行多项式拟合得到其系数,经过简单的变换即可获得多项式相位信号的参数。文中利用RPRG+ICCD分离多分量多项式相位信号得到各个分量的瞬时频率,再使用RANSAC进行多项式拟合,最终实现了多项式相位信号的参数估计。实验结果表明,文中提出的方法能够实现在时频域部分重叠的多分量多项式相位信号的分离和参数估计。

关 键 词:多项式相位信号  多分量信号  参数估计  脊路径重组  时频信号分离  随机采样一致性  
收稿时间:2019-01-15

Parameters Estimation for Multi-Component Polynomial Phase Signal by Combining RPRG and ICCD with RANSAC
CHENG Yongliang,SHAO Jie,ZHAO Yihe.Parameters Estimation for Multi-Component Polynomial Phase Signal by Combining RPRG and ICCD with RANSAC[J].Electronic Science and Technology,2020,33(2):66-70.
Authors:CHENG Yongliang  SHAO Jie  ZHAO Yihe
Institution:School of Electronic Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
Abstract:The detection and parameter estimation of multi-component polynomial phase signals is a major difficulty in polynomial phase signal processing. Time-frequency analysis method can characterize the instantaneous frequency of polynomial phase signals by time-frequency distribution. The coefficients of the extracted instantaneous frequencies can be obtained by polynomial fitting. Finally, the parameters of the polynomial phase signal can be obtained through a simple transformation. In this study, the multi-component polynomial phase signal was separated by RPRG+ICCD to obtain the instantaneous frequency of each component, and then RANSAC was used for polynomial fitting. Finally, the parameter estimation of the polynomial phase signal was realized. The experimental results demonstrated that the proposed method could achieve the separation and parameter estimation of multi-component polynomial phase signals overlapping in the time-frequency domain.
Keywords:polynomial phase signal  multi-componentssignal  parametersestimation  ridge path regroup  time frequency signal separation  random sample consensus  
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