用matlab生成包含3个以上谐波分量、冲击信号和噪声的复杂信号,绘制信号的时间序列,用FFT、STFT、小波分析和ENWO进行分析,在频域或时频域对结果进行表征
时间: 2024-02-01 18:16:22 浏览: 28
生成信号:
```matlab
Fs = 1000; % 采样频率
T = 1/Fs; % 采样间隔
t = 0:T:1-T; % 时间序列
% 三个谐波分量
f1 = 10; % 频率1
f2 = 20; % 频率2
f3 = 30; % 频率3
A1 = 1; % 幅值1
A2 = 0.8; % 幅值2
A3 = 0.5; % 幅值3
x1 = A1*sin(2*pi*f1*t);
x2 = A2*sin(2*pi*f2*t);
x3 = A3*sin(2*pi*f3*t);
% 冲击信号
t0 = 0.2; % 冲击时刻
x4 = zeros(size(t));
x4(t == t0) = 1;
% 噪声
noise = 0.2*randn(size(t));
% 合成信号
x = x1 + x2 + x3 + x4 + noise;
```
绘制信号的时间序列:
```matlab
figure;
plot(t, x);
xlabel('Time (s)');
ylabel('Amplitude');
title('Time Domain Signal');
```
使用FFT进行分析:
```matlab
% FFT
N = length(x);
f = Fs*(0:(N/2))/N;
X = fft(x)/N;
X = X(1:N/2+1);
% 绘制频谱
figure;
plot(f, abs(X));
xlabel('Frequency (Hz)');
ylabel('Amplitude');
title('FFT Spectrum');
```
使用STFT进行分析:
```matlab
% STFT
winLen = round(Fs*0.05); % 窗口长度
overlap = round(winLen/2); % 重叠长度
nfft = winLen; % FFT点数
[S, F, T] = spectrogram(x, winLen, overlap, nfft, Fs);
% 绘制时频图
figure;
imagesc(T, F, abs(S));
axis xy;
xlabel('Time (s)');
ylabel('Frequency (Hz)');
title('STFT Spectrogram');
```
使用小波分析进行分析:
```matlab
% 小波分解
wname = 'db4'; % Daubechies小波4
level = 5; % 分解层数
[C, L] = wavedec(x, level, wname);
% 小波系数频谱
figure;
for i = 1:level+1
subplot(level+1, 1, i);
if i == level+1
coef = C(1:L(level+1));
else
coef = C(L(i):L(i+1)-1);
end
plot(coef);
title(['Level ', num2str(i-1), ' Coefficients']);
end
% 重构信号
x_waverec = waverec(C, L, wname);
% 绘制重构信号
figure;
plot(t, x, 'b', t, x_waverec, 'r');
xlabel('Time (s)');
ylabel('Amplitude');
title('Wavelet Reconstruction');
legend('Original Signal', 'Reconstructed Signal');
```
使用ENWO进行分析:
```matlab
% ENWO
winLen = round(Fs*0.05); % 窗口长度
overlap = round(winLen/2); % 重叠长度
nfft = winLen; % FFT点数
[~, ~, ~, S] = enwo(x, Fs, winLen, overlap, nfft);
% 绘制时频图
figure;
imagesc(S.t, S.f, abs(S.P));
axis xy;
xlabel('Time (s)');
ylabel('Frequency (Hz)');
title('ENWO Spectrogram');
```
以上是一个简单的信号分析流程,可以根据实际情况进行调整和改进。