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Joint maximum likelihood (ML)symbol-time and carrier-frequency offset estimator in orthogonal frequency-division multiplexing(OFDM) systems.-Joint maximum likelihood (ML)symbol-time and carrier-frequency offset estimator in orthogonal frequency-divi
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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
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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)
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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)
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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)*
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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
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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
wit
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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
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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 wir
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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 nar
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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.
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极大似然估计器用于简单通信系统模拟,估计A1,A2:
s1 = x11*A1 + x12*A2
s2 = x21*A1 = x22*A2
r1 = s1 + n1
r2 = s2 + n2-Maximum likelihood estimator for a simple communication system simulation, it is estimated that A1, A2: s1 = x11* A1+ x12* A2 s2 = x21* A1 = x22
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An useful note on Maximum Likelihood Estimator(Statistical Signal Processing)
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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 locali
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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 estim
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This source code is for simulating Maximum Likelihood Estimator.
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this a maximum likelihood estimator to locate a position of a mobile station with 3 anchor nodes
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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 th
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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)
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