Description: 研究用于自适应均衡器的LMS算法的性能，数据源产生有零平均和单位方差的由符号+1和-1组成的Bernoulli的序列{I(n)}.数据源之后的信道可以用升余弦脉冲响应来模拟。
-study for the adaptive equalizer LMS algorithm performance, Data Sources have zero mean and unit variance from the -1 and a symbol of the Bernoulli sequence (I ( n)). Data Source after the channel can be raised-cosine pulse response simulation. Platform: |
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Author:lee |
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Description: 卡尔曼滤波源代码
Some brief notes
----------------
kfdemo.m is a Matlab script file to run a demonstration of the
Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4
so that it will read in either v4 or v5 Matlab).
The other files are called by kf.m (the Kalman filter) or included
as they may be useful. Normalis.m normalises data to zero-mean,
unit variance along components. I always do this before any further
analysis.
Steve Roberts 7-2-98
Platform: |
Size: 8055 |
Author:杨一鸥 |
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Description: 研究用于自适应均衡器的LMS算法的性能，数据源产生有零平均和单位方差的由符号+1和-1组成的Bernoulli的序列{I(n)}.数据源之后的信道可以用升余弦脉冲响应来模拟。
-study for the adaptive equalizer LMS algorithm performance, Data Sources have zero mean and unit variance from the-1 and a symbol of the Bernoulli sequence (I ( n)). Data Source after the channel can be raised-cosine pulse response simulation. Platform: |
Size: 1024 |
Author:lee |
Hits:
Description: 卡尔曼滤波源代码
Some brief notes
----------------
kfdemo.m is a Matlab script file to run a demonstration of the
Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4
so that it will read in either v4 or v5 Matlab).
The other files are called by kf.m (the Kalman filter) or included
as they may be useful. Normalis.m normalises data to zero-mean,
unit variance along components. I always do this before any further
analysis.
Steve Roberts 7-2-98
-Kalman filter source code Some brief notes kfdemo.m is a Matlab script file to run a demonstration of theBayesian Kalman filter. It loads file kfdemo.mat (saved as version 4so that it will read in either v4 or v5 Matlab). The other files are called by kf.m (the Kalman filter) or includedas they may be useful. Normalis.m normalises data to zero-mean, unit variance along components. I always do this before any furtheranalysis.Steve Roberts 7-2-98 Platform: |
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Author:杨一鸥 |
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Description: 仿真函数
（1），函数形式为：
其中， 是零均值单位方差的高斯噪声。输入 服从[0，1] 均匀分布，且相互独立。样本长度为1000组，将其分为三组，自由选择200组作为模型训练数据，自由选择100组作为检验数据，其余的作为长期检验数据。
（2）
其中x1~x4相互独立， ， 为白噪声
-Simulation function (1), function of the form: which is the zero mean unit variance Gaussian noise. Enter the subject [0,1] uniformly distributed and independent of each other. Sample length of 1000 group, will be divided into three groups, to free choice of 200 group as a model training data, to free choice of 100 group as test data, and the rest as a long-term test data. (2) x1 ~ x4 which are independent of each other, for white noise Platform: |
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Author:扬之江 |
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Description: pcm均匀pcm与非均匀pcm
一。产生长度为500的零均值，单位方差的高斯随机变量序列，用均匀pcm的方法用16电平进行量化：1）求所得的SQNR，该序列的前5个值，相应的量化值和相应的码字。2）画出量化误差（定义为输入值和量化值之间的差），同时 画出量化值作为输入值的函数的图。3）用128量化电平数重做2）题， 比较结果。-pcm uniform pcm with a non-uniform pcm. Have a length of 500 zero-mean, unit variance Gaussian random variables with a uniform method of pcm level by 16 to quantify: 1) demand from SQNR, the sequence of the first five values, the corresponding quantization value and the corresponding code word. 2) draw quantization error (defined as the import value and quantify the value of the difference between), at the same time draw to quantify the value of the import value as a function of the map. 3) 128 to quantify the number of redo-level 2) title, the results of the comparison. Platform: |
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Author:jin |
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Description: 使用下面的参数设计信道均衡器，其中使用两个独立的随机数发生器，一个用x(n)来表示，用来测试信道；另一个用v(n)来表示，用来模拟接收器中的加性白噪声的影响。序列x(n)是x(n)=±1的伯努利序列，随机变量x(n)具有零均值和单位方差。第二个序列v(n)具有零均值，其方差由信噪比决定。均衡器有11个抽头。-Use the following parameter design of channel equalizer, which uses two separate random number generator, one with x (n) to indicate, to test the channel the other with v (n) to indicate to simulate the receiver in The additive white noise. Sequence x (n) is x (n) = ± 1 of the Bernoulli sequence of random variable x (n) with zero mean and unit variance. The second sequence v (n) has zero mean, its variance decision by the signal to noise ratio. There are 11-tap equalizer. Platform: |
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Author:wanchun |
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Description: 一个非常好用的国外牛人写的kalman滤波程序，-kfdemo.m is a Matlab script file to run a demonstration of the Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4 so that it will read in either v4 or v5 Matlab).The other files are called by kf.m (the Kalman filter) or included as they may be useful. Normalis.m normalises data to zero-mean,
unit variance along components. I always do this before any further
analysis. Platform: |
Size: 7168 |
Author:滕佩夏 |
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Description: 一。产生长度为500的零均值，单位方差的高斯随机变量序列，用均匀pcm的方法用16电平进行量化：1）求所得的SQNR，该序列的前5个值，相应的量化值和相应的码字。2）画出量化误差（定义为输入值和量化值之间的差），同时 画出量化值作为输入值的函数的图。3）用128量化电平数重做2）题， 比较结果。
二。产生一个长度为500，按N(O,1)分布的随机变量序列，分别用16，128量化电平数和u=255的u律非线性进行量化，画出每种情况下量化器的误差和输入-输出关系，并求SQNR.
三。长度为500的非平稳序列a由两部分组成：前20个样本是按照均值为零和方差为400的高斯随机变量产生的，其余480个样本是根据均值和方差为1的高斯随机变量产生的，对这个序列分别用均匀pcm和非均匀pcm方法进行128电平量化，试比较两种情况下所得到的SQNR。
-One. Have a length of 500 zero mean, unit variance Gaussian random variables with uniform pcm way to quantify the level with a 16: 1) Find the income SQNR, the sequence of the first 5 values, the corresponding quantization value and the corresponding codeword. 2) Draw the quantization error (defined as the input value and quantify the difference between the values), and draw quantitative values as a function of input graph. 3) redo 128 the number of quantization level 2) title, compare the results.
II. Produce a length of 500, according to N (O, 1) distributed random variables, respectively 16,128 and the number of quantization level of u u = 255 to quantify non-linear law, draw each case quantizer error and input- output relationships, and seek SQNR.
III. Length of the non-stationary series is a 500 consists of two parts: the first 20 samples in accordance with zero mean and variance of 400 generated Gaussian random variable, and the remaining 480 samples are based o Platform: |
Size: 4096 |
Author:sun |
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Description: 用matlab设计FIR维纳滤波器
假设一个点目标在x，y平面上绕单位圆做圆周运动，由于外界干扰，其运动轨迹发生了偏移。其中，x方向的干扰为均值为0，方差为0.05的高斯噪声；y方向干扰为均值为0，方差为0.06的高斯噪声。用该滤波器处理观测信号，得到其最佳估计。-FIR Wiener filter design using matlab
Suppose a point target in the x, y plane around the unit circle in a circular motion due to external interference, its trajectory shift occurred. Which, x the direction of the interference of mean 0 and variance of 0.05 Gaussian noise y direction of the disturbance is zero mean Gaussian noise variance 0.06. Observed with the filter processing signal, to get the best estimate. Platform: |
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Author:yuxiaobei |
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Description: 维纳滤波其设计，假设一个点目标在x，y平面上绕单位圆做圆周运动，由于外界干扰，其运动轨迹发生了偏移。其中，x方向的干扰为均值为0，方差为0.05的高斯噪声；y方向干扰为均值为0，方差为0.06的高斯噪声。-Wiener filter is designed, assuming a point target in the x, y plane around the unit circle in a circular motion due to external interference, its trajectory shift occurred. Which, x the direction of the interference of mean 0 and variance of 0.05 Gaussian noise y direction of the disturbance is zero mean Gaussian noise variance 0.06. Platform: |
Size: 5120 |
Author:lixiaobo |
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Description: 2. 设 是窄带信号，定义 是在 区间上均匀分布的随机相位。 是寬带信号，它是一个零均值、方差为1的白噪音信号e(n)激励一个线性滤波器而产生，其差分方程为 。
1） 计算 和 各自的自相关函数，并画出其函数图形。根据此选择合适的延时，以实现谱线增强。
2） 产生一个 序列。选择合适的 值。让 通过谱线增强器。画出输出信号 和误差信号e(n)的波形，并分别与 和 比较。
-Computer Experiments:
1. Consider an AR process x(n) defined by the difference equation
where v(n) is an additive white noise of zero mean and variance .The AR parameters and are both real valued:
a) Calculate the noise variance such that the AR process x(n) has unit variance .Hence , generate different realization of the process x (n).
b) Given the input x (n), an LMS filter of length M = 2 is used to estimate the unknown AR parameters and . The step size is assigned the value 0.05. Compute and plot the ensemble average curve of and by averaging the value of parameters and over an ensemble of 100 different realization of the filter. Calculate the time constant according to the experiment results and compare with the corresponded theoretical value.
c) For one realization of the LMS filter, compute the prediction error
And the two tap-weight errors
and
Using power spectral plots of , show that Platform: |
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Author:lay |
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Description: Rayleigh fading with + BER Calculation for
Independent Gaussian random variables with zero mean and unit variance Platform: |
Size: 2048 |
Author:jiff |
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Description: 贝叶斯卡尔曼滤波器的实现。通过卡尔曼滤波可以将信号中的噪声去除，提取原信号。即具有还原信号的功能。-kfdemo.m is a Matlab script file to run a demonstration of the
Bayesian Kalman filter. It loads file kfdemo.mat (saved as version 4
so that it will read in either v4 or v5 Matlab).
The other files are called by kf.m (the Kalman filter) or included
as they may be useful. Normalis.m normalises data to zero-mean,
unit variance along components. I always do this before any further
analysis. Platform: |
Size: 8192 |
Author:kamhom |
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Description: Get histogram values and approximate it to get the pdf curve.independent Gaussian random variables with zero mean and unit variance
Define bin steps and range for histogram plotting Platform: |
Size: 1024 |
Author:ghassem |
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Description: 此程序用于产生零均值单位方差高斯白噪声的1000个样点，并利用周期图法和平均周期图法进行自相关的估计。(This program is used to produce 1000 sample points under the distribution of zero mean unit variance of Gaussian white noise , and the Periodogram method and average cycle diagram method are used to estimate the autocorrelation .) Platform: |
Size: 76800 |
Author:Jean1
|
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Description: 信道均衡的MATLAB程例，是一个很好的例子，对于做通信信道仿真研究的就有较高的参考和使用价值。(In this example we have a typical channel equalization scenario. We want
to estimate the transmitted sequence with 4-QAM symbols. In
order to accomplish this task we use an adaptive filter with N coefficients
The procedure is:
1) Apply the originally transmitted signal distorted by the channel plus
environment noise as the input signal to an adaptive filter.
In this case, the transmitted signal is a random sequence with 4-QAM
symbols and unit variance. The channel is a multipath complex-valued
channel whose impulse response is h = [1.1+j*0.5, 0.1-j*0.3, -0.2-j*0.1]^T
In addition, the environment noise is AWGN with zero mean and
variance 10^(-2.5).
2) Choose an adaptive filtering algorithm to govern the rules of coefficient
updating.) Platform: |
Size: 1024 |
Author:ZJL0110
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Search - zero-mean unit-variance