使用Python实现一段信号的频域分析与包络谱分析
2021-05-03 16:02:06 1KB 信号处理 Python PHM 时频转换
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Spectral Analysis of Large Dimensional Random Matrices(Second Edition) Zhidong Bai, Jack W.Silverstein 详细介绍了大维随机矩阵的特征值分布理论This book is dedicated to Professor Calyampudi radhakrishna rao's 90th Birthday Professor UIf Grenander's 87th Birthday Professor Yongquan Yins 80th Birthday and to My wife, Xicun Dan, my sons Li and steve gang, and grandsons Yongji, and Yonglin Zhidong bai My children, Hila and Idan ck W. silverstein Preface to the second edition The ongoing developments being made in large dimensional data analysis continue to generate great interest in random matrix theory in both theoret ical investigations and applications in many disciplines. This has doubtlessly contributed to the significant demand for this monograph, resulting in its first printing being sold out. The authors have received many requests to publish a second edition of the book Since the publication of the first edition in 2006, many new results have been reported in the literature. However, due to limitations in space, we cannot include all new achievements in the second edition. In accordance with the needs of statistics and signal processing, we have added a new chapter on the limiting behavior of eigenvectors of large dimensional sample covariance matrices. To illustrate the application of rmt to wireless communications and statistical finance, we have added a chapter on these areas. Certain new developments are commented on throughout the book. Some typos and errors found in the first edition have been corrected The authors would like to express their appreciation to Ms Li Hong for her help in the preparation of the second edition. They would also like to thank Professors Ying-Chang Liang, Zhaoben Fang, Baoxue Zhang, and Shurong Zheng, and Mr Jiang Hu, for their valuable comments and suggestions. They also thank the copy editor, Mr. Hal Heinglein, for his careful reading, cor rections, and helpful suggestions. The first author would like to acknowledge the support from grants NSFC
2021-04-29 01:36:43 4.37MB 大维随机矩阵 谱分析 大维随机矩阵
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学习空间谱分析DOA资料,可以使用MATLAB进行阵列的定位计算
2021-04-28 11:36:14 533KB DOA 空间谱分析
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LMS Test.Lab中文操作指南_Spectral Testing谱分析.pdf
2021-04-23 13:02:31 917KB LMS 振动
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Matlab功率谱分析程序
2021-04-21 17:37:05 4KB matlab 功率谱
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分别用小波分解、小波包分解和EMD分解处理滚动轴承故障数据,并结合Hilbert变换进行包络谱分析实现滚动轴承故障诊断。对滚动轴承故障数据进行小波阈值降噪。小波阈值降噪后分别进行小波分解、小波包分解和EMD分解。分别求出小波分解、小波包分解和EMD分解后各个频带的能量谱。再根据能量谱确定故障频带范围并对其进行信号重构。采用Hilbert变换对重构信号进行包络谱分析实现滚动轴承故障诊断。通过对滚动轴承内圈故障信号的分析验证了小波分解、小波包分解和EMD分解结合Hilbert变换进行包络谱分析的滚动轴承故障诊断方法的有效性。
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针对空间侦察中单通道宽带接收机截获到多个独立辐射源信号时的盲源分离问题,提出了一种新的单通道盲信号分离算法。该算法首先利用奇异谱分析(SSA)构建伪阵列信号,进而采用盲源分离算法(BSS)实现信号分离。仿真试验表明:该算法可以有效地分离空间侦察中的几种常用信号,对单频信号和线性调频信号构成的单通道信号,在信噪比大于6dB时,分离前后信号的相似系数大于0.9;对不同的相移键控信号构成的单通道信号,在信噪比大于4dB时,误码率小于0.01。
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给出了bark码调制信号的循环谱分析,可以估算出码片宽度,载频,幅度
2021-04-11 16:57:26 3KB 循环谱估计
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使用python进行小波分析,详细介绍请参考https://blog.csdn.net/qq_32832803/article/details/111866444
2021-04-10 01:19:22 10KB python 小波分析 功率谱分析
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项目经历介绍_数据.pptx
2021-04-09 10:01:24 1.84MB 谱分析
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