Description: In this paper, we present a novel data-based method for simultaneous Maximum Likelihood
(ML) symbol and carrier-frequency o畇et estimation in Orthogonal frequencydivision
multiplexing (OFDM) systems. Statistical properties introduced by the cyclic
pre痻, a guard space between OFDM symbols, provide su眂ient information about the
unknown parameters. It is shown that the redundancy introduced by this cyclic pre痻
allows the estimation to be performed without additional pilots. Simulations show that
the performance of the frequency estimator is applicable in a tracking mode while the
timing estimation can be used in an acquisition mode. Platform: |
Size: 512000 |
Author:ashish |
Hits:
Description: fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
Platform: |
Size: 1024 |
Author:resident e |
Hits:
Description: fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!.
Given the samples of a laplace distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = 1/(2*b)*exp(-abs(x-u)/b)
with parameters: u,b
format: result = fit_ML_laplace( x,hAx )
input: x - vector, samples with laplace distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
u,b - fitted parameters
CRB_b - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML
Platform: |
Size: 1024 |
Author:resident e |
Hits:
Description: fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML - fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!.
Given the samples of a log-normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(x) = sqrt(1/(2*pi))/(s*x)*exp(- (log(x-m)^2)/(2*s^2))
with parameters: m,s
format: result = fit_ML_log_normal( x,hAx )
input: x - vector, samples with log-normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
m,s - fitted parameters
CRB_m,CRB_s - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML Platform: |
Size: 1024 |
Author:resident e |
Hits:
Description: fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!.
Given the samples of a normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2))
with parameters: u,sig^2
format: result = fit_ML_normal( x,hAx )
input: x - vector, samples with normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
sig^2,u - fitted parameters
CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML - fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!.
Given the samples of a normal distribution, the PDF parameter is found
fits data to the probability of the form:
p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u)^2)/(2*sig^2))
with parameters: u,sig^2
format: result = fit_ML_normal( x,hAx )
input: x - vector, samples with normal distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
sig^2,u - fitted parameters
CRB_sig2,CRB_u - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type - ML Platform: |
Size: 1024 |
Author:resident e |
Hits:
Description: fit_ML_rayleigh - Maximum Likelihood fit of the rayleigh distribution of i.i.d. samples!.
Given the samples of a rayleigh distribution, the PDF parameter is found
fits data to the probability of the form:
p(r)=r*exp(-r^2/(2*s))/s
with parameter: s
format: result = fit_ML_rayleigh( x,hAx )
input: x - vector, samples with rayleigh distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
s - fitted parameter
CRB - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type- ML -fit_ML_rayleigh - Maximum Likelihood fit of the rayleigh distribution of i.i.d. samples!.
Given the samples of a rayleigh distribution, the PDF parameter is found
fits data to the probability of the form:
p(r)=r*exp(-r^2/(2*s))/s
with parameter: s
format: result = fit_ML_rayleigh( x,hAx )
input: x - vector, samples with rayleigh distribution to be parameterized
hAx - handle of an axis, on which the fitted distribution is plotted
if h is given empty, a figure is created.
output: result - structure with the fields
s - fitted parameter
CRB - Cram?r-Rao Bound for the estimator value
RMS - RMS error of the estimation
type- ML Platform: |
Size: 1024 |
Author:resident e |
Hits:
Description: In this project, we consider the problem of estimating a parameter associated with
the local oscillator leakage of a RF receiver by tone test. For this reason, an approxi-
mate maximum-likelihood (ML) estimator is proposed. It s error performance is analyzed
by Monte-Carlo (MC) simulations and compared to the theoretical Cramer-Rao Lower
Bounds. Platform: |
Size: 2048 |
Author:reza |
Hits:
Description: 文章针对低信噪比下的水下目标定位问题，建立了水下无线传感器阵列网络，该结构包括多个分布式声传感器阵列，它适应于多模态信号处理，既可以利用目标的方位信息，又可以用能量信息。文中提出了用每个阵列接收到的信号能量作为参量完成目标定位并推导了基于能量的最大似然比目标定位方法。数值仿真表明：基于该结构的能量似然函数定位方法，可以有效估计目标的位置。并且比单阵元网络的定位性能和信息传输率上有了较大的提高， 尤其是在低信噪比下情况下，可以大大减小估计的方差。-With novel underwater wireless sensor array network (UWSAN) architecture that consists of multiple distributed arrays of acoustic sensors, maximum likelihood localization based on acoustic energy is proposed for solving the source localization in underwater and low signal to noise ratio. The exactly maximum likelihood (ML) target location estimator is derived. Very impressive simulation results demonstrated the feasibility of such a new approach for underwater wireless sensor network (UWSN). And comparison with the single sensor UWSN, the performance of localization is improved, especially in low SNR. Platform: |
Size: 265216 |
Author:于文娟 |
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Description: The Phase Gradient Autofocus (PGA) algorithm
has been widely used in Spotlight Synthetic Aperture
Radar (SAR) to remove motion-induced blurs in the
images. The PGA algorithm has been proven to be a superior
autofocus method. PGA assumes a narrow beam,
which is valid for most SAR systems. However, lower altitude
SA& have large range dependencies that cannot be
ignored. A new phase estimator for PGA is introduced
and extended to allow range dependence. An ERS-1 image
of Death Valley is used in simulations comparing the
new estimator to the widely used maximum likelihood approach
and in demonstrating the range-dependent PGA
algorithm. Platform: |
Size: 296960 |
Author:yas |
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Description: Garch模型的最大似然估计计方法，基于MATLAB程序。
-The Garch model the maximum likelihood estimator design methodology, based on the MATLAB program. Platform: |
Size: 1024 |
Author:guhaih |
Hits:
Description: A new non-linear least squares (NLS) DRSS
location estimator that uses correlated shadowing information to improve performance is
introduced. The existing maximum likelihood (ML) estimator and Cram′er Rao lower bound
(CRLB) for RSS-based localization given do not account for correlated shadowing. Platform: |
Size: 2048 |
Author:ghassem |
Hits:
Description: 最大似然法（Maximum Likelihood，ML）也称为最大概似估计，也叫极大似然估计，是一种具有理论性的点估计法，此方法的基本思想是：当从模型总体随机抽取n组样本观测值后，最合理的参数估计量应该使得从模型中抽取该n组样本观测值的概率最大，而不是像最小二乘估计法旨在得到使得模型能最好地拟合样本数据的参数估计量。-Maximum likelihood method (Maximum Likelihood, ML), also known as maximum likelihood estimation, also known as maximum likelihood estimation, is a kind of theoretical point estimation method, the basic idea of this method is: when random the overall model after the group n sample observations, the most reasonable parameter estimator should be such that the probability of drawing the sample set of n observations of the maximum the model, rather than least squares estimation method is designed to obtain such model that best fits the sample data parameter estimator. Platform: |
Size: 1024 |
Author:向国勇 |
Hits:
Description: this a maximum likelihood estimator to locate a position of a mobile station with 3 anchor nodes Platform: |
Size: 14336 |
Author:abbas970
|
Hits:
Description: 由于测量矩阵和方位噪声之间的相关性，我们已知的发射极定位的线性最小二乘算法，如伪线性估计器，具有较大的估计偏差。本文提出了一种新的基于闭型的发射器定位算法，该算法克服了这种偏倚，利用了比定位估计的辅助变量。通过计算机模拟，新算法的性能优于伪线性估计器，同时具有与计算成本更高的极大似然发射器相同的性能。(Because of the correlation between the measurement matrix and azimuth noise, we have known that the linear least squares algorithm of emitter positioning, such as pseudo linear estimator, has a large estimation deviation. In this paper, a new kind of closed - based emitter localization algorithm is proposed, which overcomes this bias, and USES the auxiliary variable of the position estimation. Through computer simulation, the performance of the new algorithm is better than that of pseudo-linear estimator, and it has the same performance as the maximum likelihood emitter with higher computational cost.) Platform: |
Size: 796672 |
Author:龙啸九天
|
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Description: 本程序是利用电力系统状态估计的投影统计实现广义最大似然估计(This procedure is the use of power system state estimation projection statistics to achieve generalized maximum likelihood estimation) Platform: |
Size: 162816 |
Author:meng2meng
|
Hits:
Description: Fundamentals of Statistical Signal Processing: Estimation Theory
In this project, we consider the problem of estimating a parameter associated with
the local oscillator leakage of a RF receiver by tone test. For this reason, an approxi-
mate maximum-likelihood (ML) estimator is proposed. It's error performance is analyzed
by Monte-Carlo (MC) simulations and compared to the theoretical Cramer-Rao Lower
Bounds. Platform: |
Size: 3307 |
Author:xinghezhifeng |
Hits:
Search - maximum likelihood estimator