Spectral Estimation of Periodically Moving Part Modulation Based on AIDME Algorithm  

一种新的周期性运动部件调制谱估计方法(英文)

作　　者：冯孝斌 黄培康 肖志河 

机构地区：[1]National Electromagnetic Scattering Laboratory, P.O.Box 142-207, Beijing 100854, China

出　　处：《Chinese Journal of Aeronautics》2004年第3期176-180,共5页中国航空学报（英文版）

摘　　要：The Periodicallg Moving Part Modulation (PMPM) for the moving parts in targetprovides important signatures for target recognition. However, most radars operate inmultiple-target mode and can only get discontinuous clusters of the returned pulses, which makes itextremely difficult to extract PMPM signature from the echoes. This paper puts forward theAlternative Iteration Deconvolution based on Minimum Entropy criteria (AIDME) for spectralestimation of extended target's echoes, utilizing the special feature that the PMPM spectra usuallyhave simple structures. Experimental results show that this method can effectively eliminate thesevere influence caused hy the convolution kernel and gain a satisfactory spectral estimation thatapproaches to the true spectrum.The Periodicallg Moving Part Modulation (PMPM) for the moving parts in targetprovides important signatures for target recognition. However, most radars operate inmultiple-target mode and can only get discontinuous clusters of the returned pulses, which makes itextremely difficult to extract PMPM signature from the echoes. This paper puts forward theAlternative Iteration Deconvolution based on Minimum Entropy criteria (AIDME) for spectralestimation of extended target's echoes, utilizing the special feature that the PMPM spectra usuallyhave simple structures. Experimental results show that this method can effectively eliminate thesevere influence caused hy the convolution kernel and gain a satisfactory spectral estimation thatapproaches to the true spectrum.

关 键 词：moving part MODULATION DECONVOLUTION spectral estimation 

分 类 号：TN957[电子电信—信号与信息处理]