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

2 views (last 30 days)
Cakil on 28 Nov 2023
Commented: Star Strider on 30 Nov 2023
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!

Star Strider on 28 Nov 2023
The cutoff frequency of the lowpass filter is too high.
Try these —
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
.
Cakil on 30 Nov 2023
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.
Star Strider on 30 Nov 2023
As always, my pleasure!
Your interpretation of the spectrogram is correct.

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