Description: 这软件是用pisarenko方法、music方法、esprit方法来估计频率。-This software is used pisarenko way, music methods, esprit method to estimate the frequency. Platform: |
Size: 1024 |
Author:李署哲 |
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Description: 在计算机上产生一组实验数据，首先产生一段零均值白噪声数据u（n）,令功率为 ，让u(n)通过一个三阶FIR：
得到y(n). .y(n)上加三个实正弦信号f1’=0.1,f2’=0.25,f3’=0.26调整 和正弦信号幅度信噪比大致为10dB,50dB,50dB. (1) 令N=256,描绘xn波形；
(2)得出真实功率谱密度 .
(3) 利用此实验数据Pisarenko谐波分解法估计该实验数据的正弦频率及幅度。-On the computer to generate a set of experimental data, first generate a zero mean white noise data u (n), so that power, let u (n) through a third-order FIR: get y (n).. Y (n) added three real sinusoidal signal f1 ' = 0.1, f2' = 0.25, f3 ' = 0.26 and sine signal amplitude adjustment signal to noise ratio is approximately 10dB, 50dB, 50dB. (1) that N = 256, depicted xn waveform (2) the true power spectral density obtained. (3) using this experimental data, Pisarenko harmonic decomposition method to estimate the experimental data of the sinusoidal frequency and amplitude. Platform: |
Size: 208896 |
Author:hx |
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Description: 用Pisarenko谐波分解法估计一组实验数据的正弦频率及幅度。得出真实功率谱密度。-Pisarenko harmonic decomposition method with a set of experimental data estimate the sinusoidal frequency and amplitude. The true power spectral density obtained. Platform: |
Size: 2048 |
Author:hx |
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Description: 对噪声信号中的正弦信号，通过Pisarenko谐波分解方法、Music算法和Esprit算法进行频率估计，信号源是： 其中， ， ， ； 是高斯白噪声，方差为 。使用128个数据样本进行估计。 1、用三种算法进行频率估计，独立运行20次，记录各个方法的估计值，计算均值和方差； 2、增加噪声功率，观察和分析各种方法的性能。-Sinusoidal signal in the noise signal through the Pisarenko harmonic decomposition method, Music algorithms and Esprit frequency estimation algorithm, the signal source is: where,,, a Gaussian white noise, variance. 128 data samples used to estimate. 1, with three kinds of frequency estimation algorithm, run independently 20 times, record the estimated value of each method to calculate the mean and variance 2, the increase in noise power, observation and analysis of the performance of various methods. Platform: |
Size: 2048 |
Author:gab |
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Description: 原创_pisarenko算法，现代谱估计方法的一种，其在工程中应用很广-Original _pisarenko algorithm, a kind of modern spectral estimation method, which is widely used in engineering Platform: |
Size: 1024 |
Author:曹仲晴 |
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Description: 功率谱估计的各种方法的matlab代码
包括AR模型，ESPRIT,周期图谱，MUSIC，PISARENKO等方法-Power spectrum estimation method matlab code, including the method of AR model, ESPRIT, the cycle map, the MUSIC, PISARENKO etc. Platform: |
Size: 3072 |
Author:wangqi |
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Description: 对于噪声中的正弦信号，通过周期图法、AR模型法、Pisarenko谐波分解算法、MUSIC算法和ESPRIT算法进行频率估计，使用N=128个数据样本进行估计.
-By the periodogram method, AR model method, the Pisarenko harmonic decomposition algorithm, MUSIC algorithm and the ESPRIT algorithm for sinusoidal signals in noise, frequency estimation, N = 128 data samples to estimate. Platform: |
Size: 3072 |
Author:沈年凤 |
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Description: 特征空间方法是现代谱估计的重要方法之一。针对噪声中的复正弦信号的模型，通过特征矢量的方法进行信号空间的分解。通过将信号空间分解为谐波信号空间和噪声信号空间这两个相互正交的空间的方法进行谱估计。Pisarenko,Music,ESPRIT算法都是这一类方法的重要算法。我们通过对不同算法进行比较分析不同算法在各种状况下的表现性能。并针对ESPRIT算法的各种改性和提升进行对比和测试。-Feature space is one of the modern spectral estimation method. Complex sinusoidal signal for the noise model, by the method of the feature vector for the decomposition of the signal space. By decomposition of the signal space to the space harmonic signal and the noise signal space, the method of the two mutually orthogonal spatial spectrum estimation. Pisarenko, MUSIC, ESPRIT algorithm is an important algorithm of this type of method. Different algorithms, our comparative analysis of the performance of different algorithms performance under a variety of conditions. And for a variety of modification and upgrading of the ESPRIT algorithm to compare and test. Platform: |
Size: 5120 |
Author:韩晶晶 |
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Description: 频率估计的几种算法，包括music算法，rife算法，周期图法，pisarenko,levenson法-Frequency estimation of several algorithms, including music algorithm, rife algorithm, periodogram, pisarenko, levenson France Platform: |
Size: 6144 |
Author:wanghongwei |
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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: Pisarenko谐波分解算法,基于matlab平台实现,是路径规划的实用方法。- Pisarenko harmonic decomposition algorithm, Based on matlab platform, Is a practical method of path planning. Platform: |
Size: 5120 |
Author:王全祥 |
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Description: Pisarenko谐波分解算法,使用大量的有限元法求解偏微分方程，一个师兄的毕设。- Pisarenko harmonic decomposition algorithm, Using a large number of finite element method to solve partial differential equations, A complete set of brothers. Platform: |
Size: 5120 |
Author:bun |
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Description: 皮萨论科（Pisarenko）谐波分解法,求得含噪声信号中的谐波频率-Pisanoko (Pisarenko) harmonic decomposition method, obtained with the noise signal in the harmonic frequency Platform: |
Size: 1024 |
Author:周星月 |
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