基于farrow滤波器结构的时间同步算法matlab仿真,含仿真操作录像

clc; clear; close all; warning off; addpath(genpath(pwd)); FLOOR = -85; % number of frequency domain sample points within input nyquist % (controls plotting resolution) np = 100; % L = primative filter order (L+1 tap filter) L = 20; % The prototype filter is piecewise polynomial approximated using a least squares % polynomial fit through M points. M = 32; for K=5, K h = remez(L*M,[0 .5/M 1.5/M 1],[1 1 0 0]); h = h/max(h); % create the piecewise polynomial approximation to the desired filter [P e] = construct_farrow(h,L,K); % the approximated filter is the prototype plus the error hh = h+e; % plot the prototype filter subplot(5,1,1); plot(h); hold on; % overlay the polynomial approximation plot(hh,'g'); hold off; % plot the prototype frequency response [H f] = freqz(h,M,M*np); subplot(5,1,2); plot(f,max(20*log10(abs(H)),FLOOR)); hold on; % overlay the approximated frequency response HH = freqz(hh,M,M*np); plot(f,max(20*log10(abs(HH)),FLOOR),'g'); hold off; % create the input impulse x = zeros(1,L); x(1) = 1; % set the initial conditions x0 = zeros(1,size(P,1)-1); t0 = 0; if 1, [h t N t0 x0] = asrc_farrow(x,1/M,t0,x0,P); else, [h t N t0 x0] = asrc_farrow_loop(x,1/M,t0,x0,P); end; % plot the approximated impulse response and overlay the filter output subplot(5,1,3); plot(hh); hold on; plot(h,'g'); hold off; % plot the magnitude response [H,f] = freqz(h/M,1,M*np); subplot(5,1,4); % first drop a marker at the original nyquist plot([pi/M pi/M],[FLOOR 0],'g'); hold on; % plot dB magnitude with a -60dB floor plot(f,max(20*log10(abs(H)),FLOOR)); hold off; % plot the phase response subplot(5,1,5); plot(f,angle(H)); hold on; % zoom in on the passband plot(f(1:np)*M,angle(H(1:np)),'r'); hold off; % pause; end;