Matlab Accelerometer Data to Velocity V2.0
Matlab Accelerometer Data to Velocity V2.0
(OP)
thread384-296403: Matlab Accelerometer Data to Velocity
The aforementioned thread presents a code that deals with a normal sin wave quite well; however, I introduced noise into the code by adding a 2.5hz sin wave, 5hz sin wave and 25hz sin wave, which produced some unexpected results. See the attached graph for further details, the green line is direct integration of the signal and the blue line was the result of the FFT method. Any help on how to "twist" the FFT results so that it fits better would be greatly appreciated!!!
The aforementioned thread presents a code that deals with a normal sin wave quite well; however, I introduced noise into the code by adding a 2.5hz sin wave, 5hz sin wave and 25hz sin wave, which produced some unexpected results. See the attached graph for further details, the green line is direct integration of the signal and the blue line was the result of the FFT method. Any help on how to "twist" the FFT results so that it fits better would be greatly appreciated!!!





RE: Matlab Accelerometer Data to Velocity V2.0
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(2B)+(2B)' ?
RE: Matlab Accelerometer Data to Velocity V2.0
It is something similar to setting the lower frequency components to zero. Except don't cut the magnitudes off sharply. Instead, multiply the fourier coefficients by some function which is 0 at zero frequency and then changes smoothly from 0 to 1 around the transition frequency. This avoids Gibbs-effect related phenomena, which could cause ringing in your time domain.
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(2B)+(2B)' ?
RE: Matlab Accelerometer Data to Velocity V2.0
Numerical time domain integration always leads to tears...
M
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Dr Michael F Platten
RE: Matlab Accelerometer Data to Velocity V2.0
Been there, done that. Trying to estimate transient clearances betwteen components using acceleromters on each. Plasticene was a lot better.
- Steve
RE: Matlab Accelerometer Data to Velocity V2.0
1 – In time domain: Multiply time waveform by smooth envelope function in time domain prior to FFT (for example Hanning)
2 – In frequency domain: Multiply FFT by smooth HP filter function, perhaps something like
1-(1/[1+<w/wc>^<2*n>]) where n is even integer.. where wc is cuttoff and 2n is even integer... higher gives sharper cutoff.
Then of course multiply by (1/(j*w)) and iFFT
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(2B)+(2B)' ?
RE: Matlab Accelerometer Data to Velocity V2.0
Of course windowing of any kind is a non-linear process. The precision way which I used (though not available except in controlled circumstances) is to make your signals naturally leakage free. You can do this by gradually ramping up the excitation, then gradually ramping it down at the end. Make sure that you carry on recording the responses until they die away completely.
M
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Dr Michael F Platten
RE: Matlab Accelerometer Data to Velocity V2.0
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(2B)+(2B)' ?
RE: Matlab Accelerometer Data to Velocity V2.0
RE: Matlab Accelerometer Data to Velocity V2.0
Cheers
Greg Locock
New here? Try reading these, they might help FAQ731-376: Eng-Tips.com Forum Policies http://eng-tips.com/market.cfm?
RE: Matlab Accelerometer Data to Velocity V2.0
RE: Matlab Accelerometer Data to Velocity V2.0
You might try your entire process (FFT, iFFT) excluding the 1/(2*pi*f*1i) integration to see if you get back what you started with. I'm betting you won't.
You probably already figured that out. Sorry if I'm stating the obvious.
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(2B)+(2B)' ?
RE: Matlab Accelerometer Data to Velocity V2.0
well then it seems like 3 people disgaree with you. Try it.
Cheers
Greg Locock
New here? Try reading these, they might help FAQ731-376: Eng-Tips.com Forum Policies http://eng-tips.com/market.cfm?