Description: This function provides an ARMA spectral estimate which is maximum entropy satisfying correlation constraint (number of poles)and cepstrum
constrains (number of ceros)
The function requires 3 inputs: Input signal, Order of denonominator, Order of Numerator and the output variable are: numerator coeffients, denominator coeficients and square-root of input noise power.
ARMA_LAST function requires spa_corc.m file to compute the autocorrelation matrix Platform: |
Size: 1961 |
Author:joy |
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Description: Fast Algorithm estimating the number of sinusoids in a white Gaussian noise. This algorithm use a sub-space method based on chi-square statistics of eigen values of the Autocorrelation Matrix. Platform: |
Size: 7080 |
Author:wuming |
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Description: This function provides an ARMA spectral estimate which is maximum entropy satisfying correlation constraint (number of poles)and cepstrum
constrains (number of ceros)
The function requires 3 inputs: Input signal, Order of denonominator, Order of Numerator and the output variable are: numerator coeffients, denominator coeficients and square-root of input noise power.
ARMA_LAST function requires spa_corc.m file to compute the autocorrelation matrix Platform: |
Size: 2048 |
Author:joy |
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Description: 计算信号的自相关矩阵的matlab程序，主要用于信号的实时分析。-Calculation of the signal autocorrelation matrix matlab program, mainly for real-time signal analysis. Platform: |
Size: 1024 |
Author:huadian |
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Description: Fast Algorithm estimating the number of sinusoids in a white Gaussian noise. This algorithm use a sub-space method based on chi-square statistics of eigen values of the Autocorrelation Matrix. Platform: |
Size: 7168 |
Author:wuming |
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Description: the paper I uploaded was submitted in my DSP2 class. The paper discusses the results of the simulation based on the paper of Zatman on "How Narrow is Narrowband?" It investigates the second eigenvalue parameter of the received signal that determines its "narrowbandness". Zatman explains that once the second eigenvalue of the signal autocorrelation matrix rises above the noise floor, the signal BW becomes non-zero.-the paper I uploaded was submitted in my DSP2 class. The paper discusses the results of the simulation based on the paper of Zatman on "How Narrow is Narrowband?" It investigates the second eigenvalue parameter of the received signal that determines its "narrowbandness". Zatman explains that once the second eigenvalue of the signal autocorrelation matrix rises above the noise floor, the signal BW becomes non-zero. Platform: |
Size: 222208 |
Author:mocordel |
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Description: ast Algorithm estimating the number of sinusoids in a white Gaussian noise. This algorithm use a sub-space method based on chi-square statistics of eigen values of the Autocorrelation Matrix. Platform: |
Size: 7168 |
Author:victor728 |
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Description: 维纳噪声抑制，1、 实验原理是利用信号之间的相关性进行信号估计，从而将噪声滤除；
2、 辅助观测信号的噪声抑制可利用Wiener-Hopf方程 求出维纳滤波器的系数 ，式中 是 的自相关阵， 是输入信号 和滤波器输入 之间的互相关矢量；
-Wiener noise suppression, 1, experiment is the use of the correlation between the signals for signal estimation, which will filter out the noise 2, the auxiliary noise suppression observed signal Wiener-Hopf equation can be obtained by Wiener filter coefficients, where is the autocorrelation matrix, is the input signal and filters the cross-correlation between the input vector Platform: |
Size: 13312 |
Author:sag |
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Description: 信号处理输入相关矩阵的创建程序以及傅里叶变换-Signal processing procedures for the creation of the input correlation matrix and Fourier transform Platform: |
Size: 1024 |
Author:张 |
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Description: 求信号自相关矩阵的函数，输入为信号，输出为信号的自相关矩阵。-The autocorrelation matrix for the signal function, input signal, the output signal autocorrelation matrix. Platform: |
Size: 28672 |
Author:极限王子 |
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Description: 随机过程： ，其中 是均值为零、方差为1的白噪声， 、 是相互独立并在 上服从均匀分布的随机相位。采用AIC和MDL准则估计信号源个数，并且画出相应的MUSIC频率估计谱线。
要求：信号样本数为1000，估计的自相关矩阵为8阶。
-Stochastic process: where is zero mean and variance 1 white noise, are independent and uniformly distributed on a random phase. Estimated using the AIC and MDL principle source number, and draw lines corresponding MUSIC frequency estimation. Requirement: the signal samples is 1000, the estimated autocorrelation matrix of order 8. Platform: |
Size: 206848 |
Author:zxf |
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Description: 负正弦加白噪声随机过程下使用MUSIC方法进行信号频率估计仿真，信号样本数去1000，估计的自相关矩阵为8阶，分别采用AIC和MDL准则估计信号源个数，并画出相应的MUSIC频率估计谱线。-Negative sine plus white noise random process method using MUSIC frequency estimation simulation, signal samples to 1000, the estimated autocorrelation matrix of order 8, AIC and MDL criteria were used to estimate the number of signal sources, and draw the corresponding MUSIC frequency spectrum estimation. Platform: |
Size: 9216 |
Author:zxf |
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Description: LS（Least Square，最小二乘法）算法是信道估计算法中最基本的算法之一。在MIMO-OFDM系统中，经常选择LS算法或其改进算法对信道进行估计。利用LS算法设计信道估计器，算法结构简单易于实现，且估计器不需要知道信道中各个收发天线对之间的自相关矩阵和噪声。-LS (further Square, Least Square) algorithm is one of the most basic algorithm of channel estimation algorithm. In MIMO- OFDM systems, often choose LS algorithm and its improved algorithm for channel estimation. Use of channel estimator and the LS algorithm design algorithm structure simple and easy to implement, and the estimator does not need to know in the channel between the sending and receiving antenna for autocorrelation matrix and noise.
Platform: |
Size: 2048 |
Author:su |
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Description: Capon Bartlet Music
MUSIC estimates the frequency content of a signal or autocorrelation matrix using an eigenspace method. This method assumes that a signal, x(n), consists of p complex exponentials in the presence of Gaussian white noise. Given an M \times M autocorrelation matrix, \mathbf{R}_x, if the eigenvalues are sorted in decreasing order, the eigenvectors corresponding to the p largest eigenvalues (i.e. directions of largest variability) span the signal subspace. The remaining M-p eigenvectors span the orthogonal space, where there is only noise. Note that for M = p + 1, MUSIC is identical to Pisarenko harmonic decomposition. The general idea is to use averaging to improve the performance of the Pisarenko estimator. Platform: |
Size: 1024 |
Author:Said |
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Description: MUSIC estimates the frequency content of a signal or autocorrelation matrix using an eigenspace method. This method assumes that a signal, x(n), consists of p complex exponentials in the presence of Gaussian white noise. Given an M \times M autocorrelation matrix, \mathbf{R}_x, if the eigenvalues are sorted in decreasing order, the eigenvectors corresponding to the p largest eigenvalues (i.e. directions of largest variability) span the signal subspace. The remaining M-p eigenvectors span the orthogonal space, where there is only noise. Note that for M = p + 1, MUSIC is identical to Pisarenko harmonic decomposition. The general idea is to use averaging to improve the performance of the Pisarenko estimator. Platform: |
Size: 1024 |
Author:Said |
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Description: 基于RLS算法的数据预测与MATLAB实现——
递归最小二乘（RLS)算法是一种典型的数据处理方法，递归最小二乘（RLS)算法在信号自适应滤波分析中广泛应用，递归最小二乘（RLS)算法收敛速度快，且对自相关矩阵特征值的分散性不敏感，然而其计算量较大。-RLS algorithm based on forecast data and MATLAB realization Recursive Least Squares (RLS) algorithm is a typical method of data processing, recursive least squares (RLS) algorithm is widely used in signal analysis, adaptive filtering, recursive least squares (RLS) algorithm converges fast, and dispersion of autocorrelation matrix eigenvalue insensitive, but its large amount of calculation Platform: |
Size: 2048 |
Author:唐凯 |
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Search - autocorrelation matrix