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Algo for rms computation?

ggabe

I’m entertaining a true rms computation on MSPM0G3507 for the 10Hz…100KHz range. 

The MCU is limited to 32K RAM, so I’d change the sampling frequency if I can determine the signal is periodic and in the lower frequency range.

Is there any algorithm that aims capture N periods of a periodic signal? 

Should I be better of by going into frequency domain with a proper window function, and possibly trying multiple sampling rates to choose the proper range?

Parents

  • What's wrong with:

    sample (at 500kHz)       s

    square (at 500kHz)       sq = s * s

    mean (at 500kHz)         m = m + ( (sq - m) /  Ki)

    square root (at read out rate)  rms = m ^ 0.5

    Division by a constant can be done by multiplication by a constant and a shift operation.

    So you need 3 multiplies, 1 add, 1 subtract and 1 shift at sample speed, and the square root at read out speed.

    MK

Reply

  • What's wrong with:

    sample (at 500kHz)       s

    square (at 500kHz)       sq = s * s

    mean (at 500kHz)         m = m + ( (sq - m) /  Ki)

    square root (at read out rate)  rms = m ^ 0.5

    Division by a constant can be done by multiplication by a constant and a shift operation.

    So you need 3 multiplies, 1 add, 1 subtract and 1 shift at sample speed, and the square root at read out speed.

    MK

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  • in reply to michaelkellett

    I think the algorithm with Ki used to be referred as exponential decay averaging. The alternative, the moving averaging has a sensitivity when the weight of the partial periods start to influence the average. It happens on the lowest end of the spectrum.

    My gut feeling is the exponential decay has the same problem, probably tunable by Ki for the expense of return to zero time, when the signal is removed.

    To address the low frequency accuracy concerns, I would experiment with reducing the sampling rate, if the signal is periodic and not much spectral power over let's say fsample/200. This is where autocorrelation and/or FFT comes in. Luckily, it's not much trouble to simulate in a Jupyter notebook.

  • in reply to ggabe

    I'll be very interested to see how you get on with this. My own (limited) experiments in this direction have suggested that the ex. dec. has the advantage of stability and predictability but that more tuned methods which make assumptions or self modifications based on the signal may give very good results with some signals but can be caught out by others. My work was done on ST32H7 series processors which have the advantage of 16bit ADCs (not the best 16 bits I've ever seen) and the 400MHz 64 bit FP possibly makes up for the lack of the accelerator.

    I'm still working on the indirectly heated thermistor concept (which is an almost perfect physical analogue of the process I described above) but in reality it trades one kind of complexity (ADC, memory and maths) for a probably more expensive complexity in terms of insulation , heaters and thermal control Relaxed. But I have other reasons for being interested in the thermal control aspects so it remains a useful project to me.

    MK