Filter out misalignment of sensor signal

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Konvictus177 2021-7-9

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评论： Star Strider 2021-7-10

Hi,

I have taken measurements with a lasersensor that moves along an axis and takes distance measurements to a wall for each position (1, 2, 3, ...). It seems that the axis that the sensor is mounted on is not perfectly parallel to the wall so that I can see a trend which you can see in the image below. The blue line is my acquired signal, the red lines describe the signal within a measurement position (X number of points for each measurement position).

How can I filter out the misaligned sensor axis and simulate a perfectly aligned axis that is parallel to the wall?

Thanks.

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Star Strider 2021-7-9

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I have no idea what ‘perfectly aligned axis’ means.

If you want to remove the baseline trend, the best option for this signal is likely a highpass or bandpass filter.

If you want a different result, please be specific.

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Star Strider 2021-7-10

在 MATLAB Online 中打开

I am not certain how you calculated the values in the earlier plot image.

I developed this code for you to experiment with to see what works, since I have no idea what information you want in the filtered signal:

LD = load('DataZ.mat');
DataZ = LD.DataZ;
L = numel(DataZ);
x = linspace(0, L-1, L);
figure
plot(x, DataZ)
grid
Fs = 1/x(2);
Fn = Fs/2;
N = 2^nextpow2(L);
DataZm = DataZ-mean(DataZ);
FTDZ = fft(DataZm,N)/L;
Fv = linspace(0, 1, fix(numel(FTDZ)/2)-1)*Fn;
Iv = 1:numel(Fv);
figure
plot(Fv, abs(FTDZ(Iv))*2)
grid
xlim([0 0.025])
Flo = 6E-3;                                             % Low Frequency Cutoff
Fhi = 1.5E-2;                                           % High Frequency Cutoff
[DataZfilt,DF] = bandpass(DataZ, [Flo Fhi], Fs);
figure
plot(x, DataZfilt)
grid

Please experiment with this — specifically ‘Flo’ and ‘Fhi’ — to see if it produces the result you want (that I still do not have any idea about). It requires the Signal Processing Toolbox and MATLAB 2018a or later. (I can code an equivalent filter if you have an earlier version of the Toolbox.) The first figure is the original signal, the second is the Fourier transform (that can help guide the frequency values for the filter), and the third is the filtered signal.