How the increase the affect of lowpass filter on a field data?

Question

Cakil 2023-11-28

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2053112-how-the-increase-the-affect-of-lowpass-filter-on-a-field-data

评论： Star Strider 2023-11-30

采纳的回答： Star Strider

T35.mat

在 MATLAB Online 中打开

Hello,

I would like to apply lowpass filter to my temperature data to remove the high frequency noise. I have applied simply 'lowpass' and could not see a significant affect on my data. I have seen several question regarding the lowpass but I am quite new to this area, so maybe missed the point.

sampling_rate=10; %seconds
fs=1/sampling_rate; %Hz
fpass=0:0.01:fs/2;
 
T35_lp=lowpass(T35,0.01,fs);

Here you can see the applied filter and original data below. They are almost same.

Is there any way to improve the affect of lowpass filter? I appreciate any suggestions.

Thanks!

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Answer 1

Star Strider 2023-11-28

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2053112-how-the-increase-the-affect-of-lowpass-filter-on-a-field-data#answer_1361532

在 MATLAB Online 中打开

T35.mat

The cutoff frequency of the lowpass filter is too high.

Try these —

LD = load('T35.mat');

T35 = LD.T35;

sampling_interval = 10;

Fs = 1/sampling_interval;

L = numel(T35);

t = linspace(0, L-1, L)/Fs;

[FTT35,Fv] = FFT1(T35, t);

figure

semilogx(Fv, abs(FTT35)*2)

grid

xlabel('Frequency')

ylabel('Magnitude')

title('T35: Fourier Transform')

xlim([0 Fs/2])

Fco = 1E-5;

T35_filt1 = lowpass(T35, Fco, Fs, 'ImpulseResponse','iir');

Fco = 8E-7;

T35_filt2 = lowpass(T35, Fco, Fs, 'ImpulseResponse','iir');

figure

plot(t, T35, 'DisplayName','Original (Unfiltered)')

hold on

plot(t, T35_filt1, 'DisplayName','Filtered (F_{co} = 1\times 10^{-5})', 'LineWidth',2)

plot(t, T35_filt2, 'DisplayName','Filtered (F_{co} = 8\times 10^{-7})', 'LineWidth',2)

hold off

grid

legend('Location','best')

function [FTs1,Fv] = FFT1(s,t)

s = s(:);

t = t(:);

L = numel(t);

Fs = 1/mean(diff(t));

Fn = Fs/2;

NFFT = 2^nextpow2(L);

FTs = fft((s - mean(s)).*hann(L), NFFT)/sum(hann(L));

Fv = linspace(0, 1, NFFT/2+1)*Fn;

Iv = 1:numel(Fv);

FTs1 = FTs(Iv);

end

.

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Cakil 2023-11-30

Thank you for the brief information about how to approach and where to start to processing data. It might be confusing to decide which method to use, so it is really a valuable feedback for me. Also I will definitely using the FFT1. If I understand correctly, spectrogram is an alternative to Fourier transform to understand the frequency variation of the time series data.