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2下载:
该文件夹为Alamouti空时码的仿真程序,天线配置为2发2收。
主要程序的用途说明:
mainMIMO_OFDMA_2Tx_SER.m: 主程序,设置各种参数
gendata.m: 生成原始信息数据的程序
mod_2Tx_STBC_DFUSC.m:调制程序
gen_SUI_CorrCIR.m: 生成信道冲击响应的程序
get_perfectCE_2x2.m: 生成理想信道估计值的程序
chanSUI_corr_2x2.m: 发送信号通过SUI信道的程序
addA
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Adaptive Filter. This scr ipt shows the BER performance of several types of equalizers in a static channel with a null in the passband. The scr ipt constructs and implements a linear equalizer object and a decision feedback equalizer (DFE) object. It
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Matlab程序,用于比较线性均衡器、判决反馈均衡器(DFE)、盲最大似然序列估计均衡器(MLSE)等误码率性能。-Matlab procedures used to compare the linear equalizer, decision feedback equalizer (DFE), Blind maximum likelihood sequence estimation equalizer (MLSE), such as bit error rate performance.
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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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参数估计,最大似染发、最小二乘法,Monte_Carlo法仿真10000点
-Parameter estimation, maximum likelihood hair, least square method, Monte_Carlo 10,000 points for Simulation
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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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信号处理中参数最大似然估计的仿真程序设计-Maximum likelihood parameter estimation simulation
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针对多径效应的影响,提出了一种基于矩阵束的MIMO 雷达低仰角快速估计方法。该方法同时考虑了发射多径信号和接收多径信号,采用单样本数信号矢量构造了一个前后向矩阵束,并利用两个酉矩阵对该矩阵束进行降维处理,最后采用广义特征值分解的总体最小二乘法来估计目标角度。算法不需要估计数据协方差矩阵,可在低
信噪比和单样本数情况下,有效地克服多径效应,实现同时多目标低仰角估计,相比最大似然算法,避免了谱峰搜索,计算量小。仿真结果验证了该算法的有效性。-To overcome the multipath e
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确定性最大似然来进行DOA估计,在本设计中已经进行过仿真了。可以放心使用-For deterministic maximum likelihood DOA estimation, in this design has been simulated
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matlab实现:①递推增广最小二乘参数估计(RELS)②确定性系统的递推梯度校正参数估计(RGC)③递推最小二乘参数估计(RLS)④递推极大似然参数估计(RML)⑤递推随机牛顿参数估计(RSNA)-Matlab: (1) augmented recursive least square parameters estimation (RELS) (2) of a deterministic system recursive gradient correction parameter estima
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用于参数估计的极大似然估计法,简单,适用,方便-Maximum Likelihood method for parameter estimation,Simple, convenient, applicable
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“Applied Econometrics using MATLAB”配套的计量经济Matlab包-MATLAB code for:
1. least-squares, simultaneous systems (2SLS,3SLS, SUR)
2. limited dependent variable (logit, probit, tobit) and Bayesian variants
3. time-series (VAR, BVAR, ECM) estim
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极大似然参数估计,对于已知输入输出数据的情况下,对系统的参数进行估计。-Maximum Likelihood Parameter estimation, for the case of known input and output data to estimate the parameters of the system.
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增广最小二乘的递推算法对应的噪声模型为滑动平均噪声,扩充了参数向量和数据向量H(k)的维数,把噪声模型的辨识同时考虑进去。最小二乘法只能获得过程模型的参数估计,而增广最小二乘法同时又能获得噪声模型的参数估计,若噪声模型为平均滑动模型,,则只能用RELS算法才能获得无偏估计。当数据长度较大时,辨识精度低于极大似然法。-Augmented least squares of recursion algorithm corresponding noise model for moving average
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在统计计算中,最大期望(EM)算法是在概率(probabilistic)模型中寻找参数最大似然估计或者最大后验估计的算法,其中概率模型依赖于无法观测的隐藏变量(Latent Variable)。最大期望经常用在机器学习和计算机视觉的数据聚类(Data Clustering)领域。
-In the statistical calculations, the maximum expected (EM) algorithm parameter maximum likelihood estimate
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极大似然估计是一种很有用的办法,可以用MATLAB来实现,事实上,极大似然与最小二乘估计结果是相同的-MLE is a very useful way, you can use MATLAB to achieve, in fact, maximum likelihood and least squares estimation results are the same
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2类分类高斯模型
每个类是由一个单一的多元高斯分布的3-D建模
显示如何估计高斯均值向量和协方差矩阵的最大似然(ML)估计的基础上为每个类。
meanA和meanB代表每个类的均值,varA和varB的的代表每个类的协方差矩阵.-2-class classifier with Gaussian Models
Each class is modelled by a single 3-D multivariate Gaussian distribution
Show
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lda的matlab实现,lda is a Latent Dirichlet Allocation (Blei et al., 2001) package written both in MATLAB and C (command line interface).
This package provides only a standard variational Bayes estimation that was first proposed, but has a simple textua
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在统计计算中,最大期望(EM)算法是在概率模型中寻找参数最大似然估计或者最大后验估计的算法,其中概率模型依赖于无法观测的隐性变量。最大期望算法经常用在机器学习和计算机视觉的数据聚类(Data Clustering)领域。(In statistical computation, the maximum expectation (EM) algorithm is an algorithm to find the maximum likelihood estimation or the maximum
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