Description: 用Burg算法估计AR模型参数，进而实现功率谱估计.
形参说明：
x——双精度实型一维数组，长度为n，存放随机序列。
n--整型变量,随机序列的长度。
p--整型变量,AR模型的阶数。
a--双精度实型一维数组，长度为(p十1)。存放AR模型的系数a(0),a(1),...,a(p)。
v--双精度实型指针,它指向预测误差功率，即AR模型激励白噪声的方差。
-with Burg algorithm estimates AR model parameters, thereby realizing the power spectrum estimation. - Participation : x -- double-precision real one-dimensional arrays, length n, storage random sequence. N -- integer variables, random sequence length. P -- integer variables, AR model of order. A -- double-precision real one-dimensional arrays, length (p 10). AR storage coefficient of a model (0), a (1 ),..., a (p). V -- double precision real-pointer, it forecast errors at power, AR model that inspired the white noise variance. Platform: |
Size: 22982 |
Author:lkz |
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Description: 用Burg算法估计AR模型参数，进而实现功率谱估计.
形参说明：
x——双精度实型一维数组，长度为n，存放随机序列。
n--整型变量,随机序列的长度。
p--整型变量,AR模型的阶数。
a--双精度实型一维数组，长度为(p十1)。存放AR模型的系数a(0),a(1),...,a(p)。
v--双精度实型指针,它指向预测误差功率，即AR模型激励白噪声的方差。
-with Burg algorithm estimates AR model parameters, thereby realizing the power spectrum estimation.- Participation : x-- double-precision real one-dimensional arrays, length n, storage random sequence. N-- integer variables, random sequence length. P-- integer variables, AR model of order. A-- double-precision real one-dimensional arrays, length (p 10). AR storage coefficient of a model (0), a (1 ),..., a (p). V-- double precision real-pointer, it forecast errors at power, AR model that inspired the white noise variance. Platform: |
Size: 22528 |
Author:lkz |
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Description: 基于多窗谱法功率谱估计的谱减法语音增强程序，多窗谱功率谱估计比传统的周期图法方差效果更好，抑制音乐噪声效果更佳。
该程序对语音增强入门的研究者有参考意义。-Spectrum method based on multi-window power spectral estimate of spectral subtraction speech enhancement procedures, multi-window spectral power spectrum estimation than the traditional periodogram variance better suppress noise better music. The procedure entry on speech enhancement researchers have reference value. Platform: |
Size: 2048 |
Author:武月 |
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Description: 利用最小方差方法估计复正弦加白噪声得平稳信号的功率谱-Minimum variance estimation using complex sinusoidal plus a stationary white noise signal power spectrum Platform: |
Size: 1024 |
Author:yutan |
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Description: 求出信号噪声的方差，对干扰信号的噪声的方差进行估测。- EVAR Noise variance estimation.
Assuming that the deterministic function Y has additive Gaussian noise,
EVAR(Y) returns an estimated variance of this noise. Platform: |
Size: 2048 |
Author:xucaichen |
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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: 线性离散系统的批处理最小二乘参数估计的例程，噪声为方差为1的白噪声-Batch least squares linear discrete systems parameter estimation routine, the noise is white noise variance of 1 Platform: |
Size: 1024 |
Author:李亚坤 |
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Description: 白噪声中单个正弦信号的频率检测与估计
产生，其中是一高斯白噪声，其均值为0，方差为1。用MUSIC方法估计观测数据中正弦波的频率，并给出白噪声方差()un2uσ 与复正弦波的振幅A的估计值。-A single frequency detection and estimation of the sinusoidal signal in white noise, which is a Gaussian white noise with mean 0 and variance 1. MUSIC method to estimate the frequency of sine wave in the observation data, and gives the white noise variance () estimated value of the the un2uσ complex sine wave amplitude A. Platform: |
Size: 1024 |
Author:zee |
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Description: 输入信号为方波信号+高斯白噪声，方波信号基 频为1kHz，幅值为1，高斯白噪声方差为0.2， 均值为0，采样频率为20kHz。试用周期图法和 ARMA谱估计法(SVD-TLS)估计此信号的功率 谱，并分析比较其结果-The input signal is a square wave signal+ Gaussian white noise, square wave signal fundamental frequency 1kHz, amplitude is a Gaussian white noise variance is 0.2, the mean is 0, the sampling frequency is 20kHz. Try periodogram and ARMA spectral estimation method (SVD-TLS) estimate the power spectrum of this signal, and the analysis and comparison of the results Platform: |
Size: 1024 |
Author:徐强 |
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Description: 输入信号为方波信号+高斯白噪声，方波信号基 频为1kHz，幅值为1，高斯白噪声方差为0.2， 均值为0，采样频率为20kHz。试用最大熵估计 法估计此信号的AR模型及其功率谱，并分析结 果-The input signal is a square wave signal+ Gaussian white noise, square wave signal fundamental frequency 1kHz, amplitude is a Gaussian white noise variance is 0.2, the mean is 0, the sampling frequency is 20kHz. Try maximum entropy estimation method to estimate the AR model of this signal and its power spectrum, and analyze the results Platform: |
Size: 2048 |
Author:徐强 |
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Description: This routine calculates the MVU estimator and plots the Cramer_Rao Lower Bound in estimating the Average of Gausian Noise (i.e. A)assuming that the variance of the noise is variable between 1 to 5 and also the number of ndependent data samples is varying 10~100 with the step of 10 Platform: |
Size: 2048 |
Author:sh |
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Description: 对于自回归滑动平均模型进行参数辨识，并对白噪声进行方差估计。运算结果具有良好的收敛性，可推广为模型辨识的通式-For auto-regressive moving average model parameter identification, and white noise variance estimation. Operation result has a good convergence, can be extended to model identification formula Platform: |
Size: 1024 |
Author:jy |
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Description: 信道估计,we will assume that the receiver knows your name, i.e., it knows s (and of course
x). Based on this knowledge, the receiver will estimate h using the MVU and MMSE method
(for the latter it is also assumed that the variance of the noise σ2 is known at the receiver).-Channel Estimation:we will assume that the receiver knows your name, i.e., it knows s (and of course
x). Based on this knowledge, the receiver will estimate h using the MVU and MMSE method
(for the latter it is also assumed that the variance of the noise σ2 is known at the receiver). Platform: |
Size: 6144 |
Author:lauren |
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Description: Estimate spectrum finite number of noisy measurements
From spectrum estimate, extract
Disturbance parameters (e.g. noise variance)
Signal parameters (e.g. direction of arrival)
Signal waveforms (e.g. sum of sinusoids)
Applications
Beamforming and direction of arrival estimation
Channel impulse response estimation
Speech compression
-Estimate spectrum finite number of noisy measurements
From spectrum estimate, extract
Disturbance parameters (e.g. noise variance)
Signal parameters (e.g. direction of arrival)
Signal waveforms (e.g. sum of sinusoids)
Applications
Beamforming and direction of arrival estimation
Channel impulse response estimation
Speech compression
Platform: |
Size: 76800 |
Author:mehran |
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Description: 盲噪声估计出现在许多图像处理应用的问题，如去噪、COM压缩、分割。在本文中，我们提出了一种新的噪声电平估计方法的基础上的主成分分析的图像块。我们表明，噪声方差可以估计为图像块协方差矩阵的最小特征值。与现有的13种方法相比，所提出的方法显示了良好的速度和精度之间的妥协。这是至少15倍的速度比类似的精度的方法，它是至少两倍的精度比其他方法。我们的方法不假定存在均匀区域的输入图像，因此，可以成功地处理图像只包含纹理。-The problem of blind noise level estimation arises
in many image processing applications, such as denoising, com-
pression, and segmentation. In this paper, we propose a new
noise level estimation method on the basis of principal component
analysis of image blocks. We show that the noise variance can
be estimated as the smallest eigenvalue of the image block
covariance matrix. Compared with 13 existing methods, the
proposed approach shows a good compromise between speed
and accuracy. It is at least 15 times faster than methods with
similar accuracy, and it is at least two times more accurate
than other methods. Our method does not assume the existence
of homogeneous areas in the input image and, hence, can
successfully process images containing only textures. Platform: |
Size: 2878464 |
Author:陈怀兵 |
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Description: 此文件实现了自适应UKF和UKF算法对运动刚体的位姿估计，采用噪声估计器在线估计过程噪声的均值和方差，避免了人为设定噪声的统计特性。(This document implements adaptive UKF and UKF algorithm to estimate pose and pose of moving rigid body, and uses noise estimators to estimate the mean and variance of process noise online, avoiding the statistical characteristics of man-made noise.) Platform: |
Size: 116736 |
Author:草莓味牛排 |
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Search - estimation noise variance