正态分布(Normal Distribution),又称高斯分布(Gaussian Distribution),是一种连续型概率分布。正态分布的概率密度函数曲线呈钟形,关于均值(μ)对称,其数学表达式为: f(x)=12πσe−(x−μ)22σ2f(x) = \frac{1}{\sqrt{2\pi}\sigma}e^{-\frac{(x-\mu)^2}{2\sigma^2}}f(x)=2πσ1e−...
Dr Kalman 的卡尔曼滤波器。下面的描述,会涉及一些基本的概念知识,包括概率(Probability),随机变量(Random Variable),高斯或正态分配(Gaussian Distribution)还有State-space Model等等。但对于卡尔曼滤波器的详细证明,这里不能一一描述。 首先,我们先要引入一个离散控制过程的系统。该系统可用一个线性随机微分方程(Linea...
% Omega = Scalar (real), Average power of LOS component % N = Scalar (real) specifying number of random number to be % generated % OUTPUTS: % X = Scalar (Column Vector if N > 1) specifying random number % generated using Shadowed Rician distribution function % % USAGE EXAMPLES: % X ...
当复信号方差为1时,x和y方差为1/2 The typical assumption for a complex-valued Gaussian random vector is to split the variance equally among the real and imaginary parts. Let the variance be sigma2. z = sqrt(sigma2/2)*(randn(1000,1)+1j*randn(1000,1)); If you have the Communications ...
The typical assumption for a complex-valued Gaussian random vector is to split the variance equally among the real and imaginary parts. Let the variance be sigma2. z = sqrt(sigma2/2)*(randn(1000,1)+1j*randn(1000,1)); If you have the Communications Toolbox, see awgn()....
% X = Scalar (Column Vector if N > 1) specifying random number % generated using Shadowed Rician distribution function % % USAGE EXAMPLES: % X = ShadowedRicianRandGen(0.279,2,0.251); % % REFERENCES: % A. Abdi, W. C. Lau, M.-S. Alouini, and M. Kaveh, 揂 new simple model ...
How to write function to generate gaussian... Learn more about gaussian, random numbers, variance MATLAB
pd = makedist('InverseGaussian','mu',mu,'lambda',lambda)然后 r = random(pd)；r就是一个满足...
定理若随机变量\(\xi \sim s\)离散分布\(\left \{ p_{i} \right \}\),即\(P(\xi =i)=p_{i}\),并且\(z \sim F_{\xi }(x)\),取\(z=x\),则\(z \sim F(x) = \sum_{i=1}^{K}p_{i}F_{i}(x)\) 证明\(z\)的分布函数为 ...
% sigma - the parameter of the Rayleigh distribution % Outputs: % noise - a vector containing the generated Rayleigh noise samples if nargin < 2 sigma = 1; % Default sigma value if not provided end % Generate Gaussian distributed random numbers with mean 0 and std deviation sigma normal_ran...