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periodogram
- 本程序是功率谱密度的仿真比较,关于三个信号源的具体情况参见《现代数字信号处理导论》上册，P202，习题5。 实验方法：周期图法、自相关法和协方差法。 -this procedure is the power spectral density of the simulation, 3 signal source on the specific circumstances, see the "modern digital signal processing" Introd
puguji
- 谱估计 (1)修正协方差法 (2)多重信号分类（MUSIC）算法 (3)ESPRIT算法 (4)皮萨论科（Pisarenko）谐波分解法 并对各算法进行分析。 解压后即可在MATLAB上运行-spectral estimation (1) the amendment of covariance (2) multiple signal classification (MUSIC) algorithm (3) ESP RIT algorithm (4) On order pi
adsp3
- AR随机过程的建模，给定一个AR过程，对其进行建模，分别利用Yule-Walker方程和协方差法进行功率谱估计-AR random process modeling, given an AR process, its modeling, respectively, Yule-Walker equations and covariance method of power spectrum estimation
Powe_Spectrum_Estimation_based_on_AR_model
- 基于AR模型的现代谱估计，分别实现自相关法，协方差法，Burg法，对比分析性能，及与经典谱估计的比较
ar
- 含有噪声的余弦序列，分别采用周‘期图法’与‘改进协方差法’估计序的功率谱
p_morden.rar
- 用Matlab实现现代功率谱估计，主要有Levinson法，Burg法，协方差法及修正协方差法，四种方法的结果放到一起进行比较，有详细的注释,Using Matlab implementation of modern power spectrum estimation, the main has Levinson Law, Burg method covariance method covariance method, and the amendment, four ways to put to
chap08
- ex6_1 ～ ex6_3二项分布的随机数据的产生 ex6_4 ～ ex6_6通用函数计算概率密度函数值 ex6_7 ～ ex6_20常见分布的密度函数 ex6_21 ～ ex6_33随机变量的数字特征 ex6_34 采用periodogram函数来计算功率谱 ex6_35 利用FFT直接法计算上面噪声信号的功率谱 ex6_36 利用间接法重新计算上例中噪声信号的功率谱 ex6_37 采用tfe函数来进行系统的辨识，并与理想结果进行比较 ex6_38 在置信度为0
ARxzxfc
- AR模型，用修正协方差法求谱估计，简单实用。-AR model, with amendments covariance spectral estimation method, simple and practical.
experiment2
- AR过程的线性建模与功率谱估计 Yule-Walker法(自相关法) 协方差法；(2) Burg方法；(3) 修正协方差法 -The linear AR process modeling and Yule-Walker power spectrum estimation method (autocorrelation method) covariance method (2) Burg method (3) modified covariance method
weifenwaitui
- 在VC环境下，演示了协方差法的模型外推算法，测试函数为： f(x)=sin(x)*sqrt(x+1)。 最后输出结果对比 -In the VC environment, demonstrate the model covariance method extrapolation method, the test function: f (x) = sin (x)* sqrt (x+1). The comparison of the final output
3
- 用卡尔曼滤波法，虽然刚开始的初始高度协方差很大为100，但通过2步之后减小到不超过1，逐渐接近于0-Kalman filtering method, although the beginning of the initial height of 100 covariance great, but by following the two-step reduced to no more than 1, and gradually close to 0
burg
- 全极点建模的Burg算法； 产生卷积矩阵； 产生协方差矩阵； 全极点建模的协方差法； MA过程的Durbin法。-All-pole modeling of the Burg algorithm generate convolution matrix generated covariance matrix all-pole modeling of the covariance method MA process, Durbin method.
language
- 高级数字信号处理，用各种方法包括music算法、burg法、布莱克曼法、协方差法、周期图法、自相关法进行谱估计，-Advanced digital signal processing, algorithms in various ways, including music, burg method, Blackman method, covariance method, periodogram, autocorrelation method, spectral estimation,
modern
- 现代谱估计方法的matlab实现，有列文森自相关法、协方差法、修正协方差法及伯格法-Matlab modern spectral estimation methods to achieve, there Levinson autocorrelation method, covariance method, modified covariance method and the Burg method
S
- 实现功率谱估计的函数： Arburg:用Burg算法估计AR模型的参数； Arcov: 用协方差法估计AR模型的参数； Armcov:用改进的协方差法估计AR模型的参数； Aryule:用Yule_Walker算法估计AR模型的参数； -To achieve a function of power spectrum estimation: Arburg: AR with Burg algorithm to estimate model parameters Arcov:
xiefangcha
- 用修正协方差法对信号进行频率估计，并通过仿真图形得出该方法的优缺点。-The modified covariance method estimates the signal frequency, and graphics obtained through the simulation of the advantages and disadvantages of the method.
pinlv
- 利用Matlab编程验证协方差法估计信号频率的性能-Validate the use of Matlab programming covariance method to estimate the frequency of performance
dsp
- AR过程的线性建模与功率谱估计 理解AR过程的产生机理，复习实验1估计自相关序列的方法。 2.利用估计出的自相关序列来求解信号的功率谱，即用周期图法来估计功率谱。 3.分别采用自相关法（Yule-Walker法），协方差法，Burg法，修正协方差法来估计功率谱，并与周期图法进行比较，分析性能孰优孰劣。 4.学习matlab在数字信号处理中的应用。 -Linear AR process modeling and power spectrum estimation
essay-report
- 通过分析AR模型功率谱估计，介绍AR模型参数提取的自相关法、Burg法和修正协方差法，并利用计算机仿真比较其性能。-By analysis of the AR model power spectrum estimation, the introduction of the AR model parameters are extracted,namely the autocorrelation method, the Burg method and the modified covariance
PSDestimation
- 此程序使用协方差法和修正协方差法进行功率谱估计。(This two programs use covariance method and modified covariance method for power spectrum estimation.)