对通用最大熵谱分析算法程序的改进
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摘要
谱估计学科的发展,极大地提高了人们对振动、冲击及所谓噪声所反映的事物的动态特性和变化过程的认识。在现代谱估计范畴中,最大熵谱分析法是一种比较好的方法,在初期的最大熵谱分析算法中比较常用的方法是伯格算法。但这种算法在许多方面还有很大的局限性,如谱线分裂、谱能量不集中,频率偏移等等。为了克服上述算法的不足之处,对现有的伯格算法进行了较大的改进——在原算法基础上加入滤波器阶数优化,同时结合了MARPLE算法,从而大大地提高了谱估计的性能。
The major problem with morden spectral estimation is how to resolve signal from data samples more effectivly. That is to say,how to improve the resolution of the spectral analysis and perform the spectral analysis more efficiently. In this paper we will dis- cuss the maximum entropy analysis method (MEAM),a method of spectral estimation with extremely high resolution. Through analysising and researching in the MEAM theorem,we have put forward a auto-optimum seeking predictive filter orders algorithm on the basis of BURG algorothm. A lot of experimental results have shown that this algorithm presents ex- tremely high resolution and it can especially be adapted to measure signal from the data sam- ples of one or more sinusoid with GAUSS WHIT E NOISE. In experiment we can completly resolve two sinusoid signals which frequency are 0.165Hz and 0.215Hz from data sample with signal to noise ratios(SNR)being -0.485db. In the following part of this paper we will further analysis another MEAM algorithm with higher resolution named MARPLE algo- rithm,put forward by S. L. MARPLE(an american)
The major problem with morden spectral estimation is how to resolve signal from data samples more effectivly. That is to say,how to improve the resolution of the spectral analysis and perform the spectral analysis more efficiently. In this paper we will dis- cuss the maximum entropy analysis method (MEAM),a method of spectral estimation with extremely high resolution. Through analysising and researching in the MEAM theorem,we have put forward a auto-optimum seeking predictive filter orders algorithm on the basis of BURG algorothm. A lot of experimental results have shown that this algorithm presents ex- tremely high resolution and it can especially be adapted to measure signal from the data sam- ples of one or more sinusoid with GAUSS WHIT E NOISE. In experiment we can completly resolve two sinusoid signals which frequency are 0.165Hz and 0.215Hz from data sample with signal to noise ratios(SNR)being -0.485db. In the following part of this paper we will further analysis another MEAM algorithm with higher resolution named MARPLE algo- rithm,put forward by S. L. MARPLE(an american)
引文
1 郑治真.数字信号处理基础.北京:地震出版社,1988
2 何振亚.数字信号处理的理论与应用.北京:人民邮电出版社,1983
3 王宏禹.随机数字信号处理.北京:科学出版社,1988
4 梁泰基.统计无线电理论.长沙:国防科技大学,1988
5 黄俊钦.随机信号处理.北京:北京航空航天大学出版社,1990
6 Marrle S L. A New Autoregressive Speotrum Analysis Algorithm. IEEE Trans. ASSP 1980; 28 (4)
7 北方交通大学信息科学研究所.近代数字信号处理通用程序.北京:科学出版社,1988
2 何振亚.数字信号处理的理论与应用.北京:人民邮电出版社,1983
3 王宏禹.随机数字信号处理.北京:科学出版社,1988
4 梁泰基.统计无线电理论.长沙:国防科技大学,1988
5 黄俊钦.随机信号处理.北京:北京航空航天大学出版社,1990
6 Marrle S L. A New Autoregressive Speotrum Analysis Algorithm. IEEE Trans. ASSP 1980; 28 (4)
7 北方交通大学信息科学研究所.近代数字信号处理通用程序.北京:科学出版社,1988
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