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How to use phase difference information to extract effective components of signal?

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I now have two lines of data, both of which contain signal components and strong noise. The signal components have the same frequency, but the frequency is unknown. And in these two lines of data, the signal components have a 180 degree phase difference. Is there any way to separate the signal components from the noise based on these two lines of data?
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大路 郑
大路 郑 on 28 May 2024 at 1:10
I have tried to subtract two lines of data directly. Unfortunately, this method does not reduce the noise, so the signal component is still not obvious after subtraction.

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Answers (1)

cui,xingxing
cui,xingxing on 28 May 2024 at 5:34
Yes, you can separate the signal components from the noise using the fact that the signal components in the two lines have a 180-degree phase difference. One effective way to do this is by subtracting one line of data from the other. This will cancel out the signal components (since they are 180 degrees out of phase and will add up destructively), leaving you with a signal that primarily consists of noise. Then, you can use signal processing techniques such as filtering to isolate the signal component.
Here's a step-by-step MATLAB approach to achieve this:
  1. Subtract one line of data from the other to eliminate the signal component.
  2. Use filtering techniques to isolate the noise-free signal.
Below is the MATLAB code to demonstrate this process:
% Sample data generation (for demonstration purposes)
% Replace this with your actual data
fs = 1000; % Sampling frequency
t = 0:1/fs:1-1/fs; % Time vector
f = 50; % Frequency of the signal component
signal1 = sin(2*pi*f*t); % First signal component
signal2 = -sin(2*pi*f*t); % Second signal component with 180-degree phase shift
noise1 = 0.5*randn(size(t)); % Noise in the first line
noise2 = 0.5*randn(size(t)); % Noise in the second line
data1 = signal1 + noise1; % First line of data
data2 = signal2 + noise2; % Second line of data
% Step 1: Subtract one line of data from the other
data_diff = data1 - data2;
% Step 2: Filter the noise from the resulting data to isolate the signal component
% Design a bandpass filter to isolate the frequency of interest
d = designfilt('bandpassiir', 'FilterOrder', 4, ...
'HalfPowerFrequency1', f-10, 'HalfPowerFrequency2', f+10, ...
'SampleRate', fs);
% Apply the filter to the original data to isolate the signal component
isolated_signal = filtfilt(d, data1);
% Plot the results
figure;
subplot(3,1,1);
plot(t, data1);
title('Data 1');
xlabel('Time (s)');
ylabel('Amplitude');
subplot(3,1,2);
plot(t, data2);
title('Data 2');
xlabel('Time (s)');
ylabel('Amplitude');
subplot(3,1,3);
plot(t, isolated_signal);
title('Isolated Signal Component');
xlabel('Time (s)');
ylabel('Amplitude');

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