一种新的周期性运动部件调制谱估计方法

目标内部运动部件对雷达回波产生的周期性运动部件调制 (PMPM )为目标识别提供了丰富的特征信息。但由于大多数雷达均采用多目标工作方式 ,因此 ,通常情况下只能从雷达获得不连续的目标回波脉冲串 ,这极大增加了根据目标回波频谱提取PMPM特征的难度。针对含PMPM目标回波频谱具有简单结构这一特点 ,提出了基于最小熵准则的交替迭代反卷积方法(AIDME)用于扩展目标雷达回波频谱估计。结果表明 :与傅里叶谱分析方法相比 ,该方法能够有效消除卷积核函数的不利影响 ,获得逼近目标回波真实谱的良好谱估计结果

Spectral Estimation of Periodically Moving Part Modulation Based on AIDME Algorithm

The Periodicallg Moving Part Modulation (PMPM) for the moving parts in target provides important signatures for target recognition. However, most radars operate in multiple-target mode and can only get discontinuous clusters of the returned pulses, which makes it extremely difficult to extract PMPM signature from the echoes. This paper puts forward the Alternative Iteration Deconvolution based on Minimum Entropy criteria (AIDME) for spectral estimation of extended target's echoes, utilizing the special feature that the PMPM spectra usually have simple structures. Experimental results show that this method can effectively eliminate the severe influence caused by the convolution kernel and gain a satisfactory spectral estimation that approaches to the true spectrum.