去除高斯噪声滤波器

clc; close all; fs = 44100; % x = wavread('b.wav'); t = -5*pi:pi/100:5*pi; x = sin(t); x = x(:); sx = size(x,1); subplot(2,2,1); plot(x);axis([0 sx -1 1]); % 原信号FFT xf = fft(x,1024); subplot(2,2,3); plot(abs(xf)); % 添加高斯噪声 t = 0 : 1/fs : (sx-1)/fs; noise = 0.2*randn(size(x)); % 均值为0，方差为0.5的标准正态噪声 x1 = x + noise; subplot(2,2,2); plot(x1);axis([0 sx -1 1]); % 信号加噪声后的FFT xf = fft(x1,1024); subplot(2,2,4); plot(abs(xf)); % LMS自适应滤波 param.M = 50; param.w = ones(param.M, 1) * 0.1; param.u = 0.1; param.max_iter = 100; param.min_err = 0.5; [yn,err] = zx_lms(x1(:,1), x(:,1), param); figure, plot(yn) ynf = fft(yn(param.M:end), 1024); figure, plot(abs(ynf)); function [yn ,err] = zx_lms(xn, dn, param) % x 输入信号 % dn 期望输出 % param Structure for using LMS, must include at least % .w - 初始化权值 % .u - 学习率 % .M - 滤波器阶数 % .max_iter - 最大迭代次数 % .min_err - 迭代最小误差 % % y 经过滤波器后的输出信号 % error 误差输出 W = param.w; % 初始权值 M = param.M; % 滤波器阶数 if length(W) ~= M error('param.w的长度必须与滤波器阶数相同.\n'); end if param.max_iter > length(xn) || param.max_iter < M error('迭代次数太大或太小，M<=max_iter<=length(xn)\n'); end iter = 0; for k = M:param.max_iter x = xn(k:-1:k-M+1); % 滤波器M个抽头的输入 y = W.*x; err = dn(k) - y; % 更新滤波器权值系数 W = W + 2*param.u*x; iter = iter + 1; if (abs(err) < param.min_err); break; end end % 求最优时滤波器的输出序列 yn = inf * ones(size(xn)); for k = M:length(xn) x = xn(k:-1:k-M+1); yn(k) = W(:,end).'* x; end end