MATLAB信号处理仿真入门实验

    %///////////////////////////////////////////////////////////
    % DFT analyse of sampled sine signal
    %///////////////////////////////////////////////////////////
    close all;
    clear;
    clc;
    % generate 2 sampled sine signals with different frequency（生成2个不同频率的采样正弦信号）
    freq_x1         = 20.0E3        ;  % frequency of signal x1
    amp_x1          = 10            ;  % amptitude of signal x1
    freq_x2         = 30.0E3        ;  % frequency of signal x2
    amp_x2          = 10            ;  % amptitude of signal x2
    data_len        = 512           ;  % signal data length
    fs              = 512E3         ;  % sample rate（采样率）
    quant_bits      = 12            ;  % signal quant bits（信号量化比特数）
    kaiser_beta     = 8             ;  % beta of kaiser win
    
    idx_n           = [0:data_len-1];  % n index
    idx_n           = idx_n .'      ;  % we need column vector（列向量）
    idx_t           = idx_n/fs      ;  % time index
    idx_phase_x1    = 2*pi*idx_n*freq_x1/fs;  % x1 phase  index（x1相位指数）
    idx_phase_x2    = 2*pi*idx_n*freq_x2/fs;  % x2 phase  index
    x1              = amp_x1*sin(idx_phase_x1);
    x2              = amp_x2*sin(idx_phase_x2);
    % signal x is consisted of x1 and x2;
    x = x1 + x2;
    max_abs_x = max(abs(x));    %   abs-求数值的绝对值与复数的幅值  %  normalize x to (-1,1)
    x = x / max_abs_x;           % quant signal, the range is (-max_q, +max_q)
    max_q = 2^(quant_bits-1);
    x_quant = fix(x * max_q);   % plot them, use time label
    figure;
    set(gca,'fontsize',16);     % get window function data（获取窗口函数数据）（set-设置对象属性）
    win = kaiser(data_len, kaiser_beta);    % windowing the data
    win_x       =  win .* x;
    win_x_quant =  win .* x_quant;
    
    h_t1 = subplot(4,1,1);plot(idx_t, x1   );grid on;
    h_t2 = subplot(4,1,2);plot(idx_t, x2   );grid on;
    h_t3 = subplot(4,1,3);plot(idx_t, x    );grid on;
    h_t4 = subplot(4,1,4);plot(idx_t, win_x);grid on;
    title(h_t1, 'x1'        , 'fontsize', 14);
    title(h_t2, 'x2'        , 'fontsize', 14);
    title(h_t3, 'x=x1+x2'   , 'fontsize', 14);
    title(h_t4, 'windowed x', 'fontsize', 14);
    
    % perform fft
    x_q_fft         =  fft(win_x_quant)    ;    % get frequency index（指数）
    idx_freq        = -fs/2 + idx_n .* (fs / data_len);
    % shift zero frequency to the data center
    x_q_fft         =  fftshift(x_q_fft);
    % map to amptitude dB scale
    x_q_fft_abs     =  abs(x_q_fft);
    x_q_fft_abs_dB  =  20*log10(x_q_fft_abs + 1E-8);
    % normalize the spectrum from 0 dB;
    max_dB = max(x_q_fft_abs_dB);
    norm_spectrum   =  x_q_fft_abs_dB - max_dB;
    figure; plot(idx_freq, norm_spectrum);grid on;
    title('Normlized Spectrum ', 'fontsize', 14);