2015秋随机信号实验报告模板.docVIP

实验一 一、实验目的 熟悉并练习使用Matlab 的函数，明确各个函数的功能说明和内部参数的意义 二、实验内容和步骤 实验代码： A = [1 2 3; 3 3 6; 4 6 8; 4 7 7]; rand(3) randn(3) n3 = normrnd([1 2 3;4 5 6],0.1,2,3) mean(A) mean(A,2) var(A) %%%xcorr%%%%% ww = randn(1000,1); [c_ww,lags] = xcorr(ww,10,coeff); figure(7); stem(lags,c_ww) %%%%%%%%%%%%%%%%%%%%%%%%% %常用的傅立叶变换是找到在嘈杂的域 %信号下掩埋了信号的频率成分。 %考虑数据采样在1000赫兹。现有一信号 %由以下部分组成，50赫兹振幅 %为0.7的正弦和120赫兹振幅为1的正弦 %并且受到一些零均值的随机噪声的污染 %%%%%%%%%%%%%%%%%%%%%%%%%Fs = 1000; % 采样频率 T = 1/Fs; % 采样时间 L = 1000; % 信号长度 t = (0:L-1)*T; % 时间矢量 % 50赫兹正弦波与120赫兹正弦波的和 x = 0.7*sin(2*pi*50*t) + sin(2*pi*120*t); y = x + 2*randn(size(t)); % 正弦波加噪声 figure(6); plot(Fs*t(1:50),y(1:50)) %画此信号的时域图 title(Signal Corrupted with Zero-Mean Random Noise) xlabel(time (milliseconds)) %这在寻找原始信号的频率成分上是很难 %确定的。转换到频域，噪音信号Y %的傅立叶变换采取快速傅立叶变换 %(FFT)： NFFT = 2^nextpow2(L); %y长度L附近 %的幂级数 Y = fft(y,NFFT)/L; f = Fs/2*linspace(0,1,NFFT/2+1); % 单边拉普拉斯变换 plot(f,2*abs(Y(1:NFFT/2+1))) %画单边频谱图 title(Single-Sided Amplitude Spectrum of y(t)) xlabel(Frequency (Hz)) ylabel(|Y(f)|) %%%%%%%%%%%%%%%%%%%%%%%%% mu = [0:0.1:2]; [y i] = max(normpdf(1.5,mu,1)); MLE = mu(i) %%%%%%%%%%%%%%%%%%%%%%%%% p = normcdf([-1 1]); p(2) - p(1) %%%%%%%%%%%%%%%%%%%%%%%%% x = 0.1:0.1:0.6; y = unifpdf(x) %%%%%%%%%%%%%%%%%%%%%%%%% probability = unifcdf(0.75) %%%%%%%%%%%%%%%%%%%%%%%%% x = 0:0.1:3; p = raylpdf(x,1); figure(5); plot(x,p) %%%%%%%%%%%%%%%%%%%%%%%%% x = 0:0.1:3; p = raylcdf(x,1); figure(4); plot(x,p) %%%%%%%%%%%%%%%%%%%%%%%%% y = exppdf(5,1:5) %%%%%%%%%%%%%%%%%%%%%%%%% mu = 10:10:60; p = expcdf(log(2)*mu,mu) %%%%%%%%%%%%%%%%%%%%%%%%% n = 5; X = pascal(n) R = chol(X) X(n,n) = X(n,n)-1 %%%%%%%%%%%%%%%%%%%%%%%%% x = [randn(30,1); 5+randn(30,1)]; [f,xi] = ksdensity(x); figure(3); plot(xi,f); %%%%%%%%%%%%%%%%%%%%%%%%% x = -2.9:0.1:2.9; y = randn(10000,1); hist(y,x) %%%%%%%%%%%%%%%%%%%%%%%%% %求y=x*log(1+x)在[0 1]上的定积分，积分 %变量为系统默认 syms x; S=x.*log(1+x) Y=int(S,x,0,