基于CEEMDAN和FRFT的多分量LFM信号参数估计

Parameter Estimation of Multi-component LFM Signal Based on CEEMDAN and FRFT

针对多分量线性调频(LFM)信号参数估计问题,提出了一种基于自适应噪声完备集合经验模态分解(CEEMDAN)与分数阶傅里叶变换(FRFT)的参数估计方法。首先对多分量LFM信号实施CEEMDAN分解,得到多个固有模态函数(IMF)分量,根据每个IMF分量的频谱幅值方差大小,筛选出有用IMF分量,随后重构有用IMF分量得到降噪后的LFM信号,由于LFM信号在FRFT域中具有能量聚焦性,可利用FRFT的分离技术和拟Newton法计算出信号的相关参数。将上述方法和传统FRFT方法对不同信噪比的多分量LFM信号进行蒙特卡罗实验对比,实验结果证明了该方法的有效性和可靠性。

Aiming at the parameter estimation of multi-component linear frequency modulation(LFM)signal,a parameter estimation method based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and fractional Fourier transform(FRFT)is proposed.Firstly,the multi-component LFM signal is decomposed by CEEMDAN to obtain multiple intrinsic mode function(IMF)components,according to the variance of the spectrum amplitude of each IMF component,the useful IMF components are screened out,then the useful IMF components are reconstructed to get the de-noised LFM signal.The LFM signal has the characteristics of energy focusing in FRFT domain,so the relevant parameters of signal can be calculated by FRFT separation technology and quasi-Newton method.The above method is compared with the traditional FRFT method by Monte Carlo experiments on multi-component LFM signals with different signal-to-noise ratios.The experimental results prove the effectiveness and reliability of the method.