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Description: ML Estimation of frequency, phase, and amplitude of a sinusoid from discrete time samples
MLEsim.m
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Size: 1150 |
Author: 老邢 |
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Description: ML Estimation of 2 PFM signals using EM and AM
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Size: 1599 |
Author: 老邢 |
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Description: doa估计经典capon算法,
简单波达方向估计算法,空间角度分辨率有限,有待进一步改进算法-doa estimation algorithm capon classic, simple DOA estimation algorithm, space limited angular resolution, need to be further improved algorithm
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Size: 1024 |
Author: liuheng |
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Description: 有关OFDM信道估计的matlab程序（其中包含英文简介和部分simulink模型）-On the OFDM channel estimation in matlab program (which includes profiles and some English simulink model)
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Size: 190464 |
Author: 桃子 |
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Description: Probability of Error Calculation of OFDM Systems With Frequency Offset
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Size: 136192 |
Author: Ashok Kumar |
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Description:
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Size: 4096 |
Author: 杨猛 |
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Description: 信道均衡
目的和原理
MLSE、 DFE、 ZF/LMMSE均衡
干扰抵消
原理
导频干扰抵消和多码道干扰抵消
HSDPA简介
主要信道
接收机结构
-Channel equalization purposes and principles of MLSE, DFE, ZF/LMMSE equalizer pilot interference cancellation principle of interference cancellation and multi-code channel interference cancellation receiver HSDPA channel structure of the main profile
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Size: 1362944 |
Author: 王琴 |
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Description: 空间谱估计 Estimation of Signals
现代信号处理方法用于DOA估计-Music DOA Esprit DOA capon ML
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Size: 2048 |
Author: yurt |
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Description: DOA估计得MATLAB代码，不敢独享，希望对大家有用-DOA estimation in MATLAB code, and not exclusive and hope for all of us
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Size: 141312 |
Author: 蒋继菲 |
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Description: MIMO系统中进行信道估计的原始代码，Matlab格式。-code with matlab format for channel estimation in MIMO system
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Size: 3072 |
Author: zhwx |
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Description: 2发1收 与 2发1收的比较
实验次数 2000次，采用LS进行信道估计，ML进行译码，导频符号长度为10
-2 receive and 2 send a send a receive 2,000 times the number of comparative experiments, using LS channel estimation, ML decoding, pilot symbol length of 10
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Size: 9216 |
Author: 李世杰 |
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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.
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Size: 512000 |
Author: ashish |
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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
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Size: 1024 |
Author: resident e |
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Description: 在雷达、声纳、通信以及振动工程等领域中经常根据离散观测值（采样序列）对正弦信号的参数进行估计。采用复信号模型给信号分析和处理带来很大方便,因此文献中通常采用复正弦信号模型。-In radar, sonar, communications and vibration engineering and other fields are often based on discrete observations (sample sequence) of the sinusoidal signal parameters estimation. Using a complex signal analysis and signal processing models to bring great convenience, it is usually the literature using a complex sinusoidal signal model.
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Size: 112640 |
Author: wuyanjun |
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Description: 基本OFDM訊號在IEEE 802.11a架構下利用pilot估測CFO整數ML估測小數和pilot估測剩餘CFO的原始碼-IEEE 802.11a OFDM signal in the basic framework of integer CFO estimation using pilot and pilot ML estimate fractional CFO estimation of the remaining source code
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Size: 2048 |
Author: 范夢葳 |
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Description: 基于循环前缀ML估计的同步分析及FPGA实现.rar-Simultaneous analysis of ML estimation based on cyclic prefix and FPGA. Rar
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Size: 374784 |
Author: lu_xy |
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Description: here data is given and estimate using ML estimation in pattern recognition
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Size: 3072 |
Author: manish |
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Description: SNR ML estimation
it can estimation SNR by estimation
SNR from -10-20db
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Size: 1024 |
Author: huang |
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