Description: 利用循环前缀的最大似然同步算法，程序正确并实现，欢迎大家前来下载。-The use of cyclic prefix of the maximum likelihood synchronization algorithm, the program correctly and to achieve, we are happy to come to download. Platform: |
Size: 4096 |
Author:李莹 |
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Description: 球形检测算法论文，性能接近最大似然算法，但是复杂度较低-Spherical detection algorithm paper, the performance near the maximum likelihood algorithm, but the complexity is low Platform: |
Size: 4191232 |
Author:王超 |
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Description: Algorithms for QAM Signal Classification Using Maximum Likelihood Approach Based on the Joint Probability Densities of Phases and amplitudes Platform: |
Size: 212992 |
Author:HASHEM |
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Description: 《数字通信》(第4版)是数字通信领域的一本经典教材,通过对概率论及随机过程的复习,详细介绍了数字和模拟信源编码、数字调制信号和窄带信号与系统的特征、加性高斯白噪声中数字通信的调制和最佳调制与检测方法、基于最大似然准则的载波相位估计和定时同步的方法、不同信道模型的信道容量及随机编码、带限信道的信号设计、受到符号间干扰恶化信号的解调与检测问题、自适应信道均衡、多信道与多载波调制、扩展频谱信号和系统、衰落信道上的数字通信。 -" Digital communication" (4th edition) is a digital communications of a classic textbook in the field, through the review process, the probability of randomly addressed, details of the digital and analog source coding, digital modulation signal and the narrowband signal and system characteristics, additive white Gaussian noise in digital communication modulation and the best modulation and detection methods, based on maximum likelihood criterion of carrier phase estimation and time synchronization method, different channel model of channel capacity and random coding, band-limited channel signal design by ISI worse signal demodulation and detection, adaptive channel equalization, multi-channel and multi-carrier modulation, spread spectrum signals and systems, fading channel digital communications. Platform: |
Size: 17589248 |
Author:ng |
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Description: 该算法包含极大似然估计的梯度算法和快速ica算法，已经调试成功过，调试时注意所给信号x是行向量还是列向量-The algorithm contains the maximum likelihood estimation of the gradient method and fast ica algorithm, has been debugged, debugging attention given signal x is a row vector or column vector Platform: |
Size: 3072 |
Author:高丽 |
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Description: 本文深入研究了复杂调制信号MPSK、MQAM的倍号子类以及常用恒包络信
号OQPSK、or／4 QPSK、MSK的调制识别披本。在鳆种基本调制识别方法即基于特征提取的方法和基于最大似然的方法的纂础上采用四种算法分别对上述信号进行识别研究。-This article studies the complex modulation signals MPSK, MQAM number of times and the common sub-class of constant envelope signals OQPSK, or/4 QPSK, MSK modulation recognition of this disclosure. Abalone species in the basic modulation recognition method that is based on feature extraction methods and maximum likelihood methods based on the Article on the foundation of four algorithms to identify each of these signals. Platform: |
Size: 2275328 |
Author:klq |
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Description: 用MATLAB编写的单目标跟踪算法程序，采用了递归式算法，包括极大似然然估计，卡尔曼滤波，扩展卡尔曼滤波和无迹卡尔曼滤波，带有注释，易于理解。-Written with the MATLAB program single-target tracking algorithm, using recursive algorithms, including maximum likelihood estimation, Kalman filtering, extended Kalman filter and unscented Kalman filter, with comments, easy to understand. Platform: |
Size: 11264 |
Author:asd |
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Description: 1 通过实验，掌握多元正态分布的最大似然估计；
2 掌握多元正态分布下的最小错误率的贝叶斯分类；
3 对其他的参数估计有更深的认识。
-1 experiment, master multivariate normal distribution maximum likelihood estimation 2 multivariate normal distribution under the minimum control error rate Bayesian classifier 3 on the other have a better understanding of parameter estimation. Platform: |
Size: 62464 |
Author:李岩 |
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Description: 最大似然估计法的matlab实现，及Crammer-lor下界的确定-Maximum likelihood estimation of the matlab implementation, and Crammer-lor to determine the lower bound Platform: |
Size: 1024 |
Author:dago |
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Description: EM algorithm is to solute the problem of parameter maximum likelihood estimation by Dempster, Laind, Rubin in 1977. The EM algorithm can estimate maximum likelihood only through incomplete data set.
-EM algorithm is to solute the problem of parameter maximum likelihood estimation by Dempster, Laind, Rubin in 1977. The EM algorithm can estimate maximum likelihood only through incomplete data set.
Platform: |
Size: 1024 |
Author:hporange |
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Description: 高频雷达 目标检测 最大似然恒虚警方法 可用于weibull分布下的检测-High-frequency radar target detection maximum likelihood constant false police weibull distribution method can be used to detect under Platform: |
Size: 1024 |
Author:刘 |
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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 |
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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 |
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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 |
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Search - maximum likelihood