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Feb12-13, 05:54 PM
P: 7

I'm having trouble with the application of filtering to real acceleration data. I have looked at a lot of recommendations, filtering through fft, butterworth high and low pass filters, and I'm not finding something thats working for my data and I'm looking for recommendations.

I think my problem is that I am looking at very slow accelerations, so the "drift" in the accelerometer is eliminated-but so is the trend in the data itself. I will be looking at the cases of relatively slow acceleration and velocity, and slow acceleration with pauses in between sequences. A low pass filter does okay-but does not provide an accurate map of displacement in the end. Using an fft seems to also eliminate the slower trend in the data.

I have also added in elimination of values less than the abolsute mean found for a static case. This seems to help a little bit, but I may be losing some data.

I have attached a couple of examples of what the problem is. I made a rectangle with the accelerometer pretty much and these are the results with no filters and a low pass filter.
First 2 are no filter. 2nd 1 is the LP filtered accel. (I can send velocity if interested-looks the same pretty much)

Are there any other filters I should be trying? Different methods of integration? (I am currently using a trapazoidal integrator). I'm pretty much looking for recommendations processing this type of data.

Thanks! (I also hope this is the correct thread for this).
Attached Thumbnails
Test1AccelUF.jpg   Test3VelUF.jpg   Test1AccelLPF.jpg  
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