主瓣谱失真度及其自估计算法  

Mainlobe Spectrum Distortion and Its Self-estimation Algorithm

作　　者：刘良兵[1] 方卫红[1] 税爱社[1] 刘军[1] 张立[1] 

机构地区：[1]重庆后勤工程学院,重庆400013

出　　处：《电子与信息学报》2013年第6期1512-1515,共4页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61201450;71101152;61271449);重庆市自然科学基金(cstc2012jjA1047;cstc2011BA2015)资助课题

摘　　要：主瓣范围内的频谱信息是频谱分析方法的主要研究对象,其中包含的噪声分量对频谱分析精度影响较大。该文首先参照SNR的数学定义提出主瓣谱失真度(MSD)概念,以定量描述频谱分析结果的可信度。然后为举例说明MSD与频谱分析误差之间的良好关联性,进行能量重心式相位测量仿真实验,结果表明:相比于SNR,MSD能更准确地跟踪该相位测量误差曲线的变化。最后给出了详细的MSD自估计算法,该算法可以在不测量频率和初相的条件下直接估计MSD,且精度较高,抗噪性、实时性好。Spectrum informations in the scope of the mainlobe are the main research object of spectrum analysis methods, in which noise component always decreases the accuracy of spectrum analysis. Firstly, the concept of the Mainlobe Spectrum Distortion （MSD）, according to the mathematical definition of SNR, is proposed to describe the confidence level of spectrum analysis results quantitatively. Then, to show the obvious relationship between MSD and the error of spectrum analysis, some phase measurements using energy gravity method are carried out as examples, whose results show that, MSD can trace the error curve of phase measurements more closely than that of SNR. Finally, a MSD self-estimation algorithm is provided in details, which can estimate MSD quickly with high accuracy and good anti-noise performance, without measureing the frequency and initial phase.

关 键 词：信号处理 频谱 信噪比 主瓣谱失真度 自估计 

分 类 号：TN911.7[电子电信—通信与信息系统]