*mean*

*adjective*- unkind or spiteful:*a mean trick**noun- Mathematics: The average value of a set of numbers.*

Many times measured analog signals must be filtered before they can be used. Sometimes it's important to remove high frequency components, from a slight low pass filter to something more drastic like constant estimation. We could say we're doing noise reduction by oversampling on many cases.

Averaging

The first method and probably the most intuitive one is by summing several samples and then calculating the mean.

Moving average

The *conservative* method would be to implement a FIR filter, leading to a moving average filter. This gives us a filtered sample for each incoming measurement. The simplest method is using a non-weighted averaging filter, where each sample gets equal importance. Here there's the bode plot corresponding to a 20-point moving average filter with sampling frequency 10kHz.

It's clear this is not a typical lowpass filter, even though it helps to remove high frequency components. Besides doing that this filter also presents a high attenuation at frequencies multiple of Fsample / N. This can be useful on some cases but it can also be an unwanted effect. Low-pass cut off frequency (-3 dB) is about Fsample / (2*N).

Averaging and subsampling

Another way is to take N samples and calculate the mean of those samples once they've been acquired. Once that happens the whole buffer is cleared and measurements are accumulated again until N new samples are received and the new mean is calculated. This is similar to reducing sampling rate but no lowpass filter is being applied to the signal, so expect to have disastrous results for a non-constant signal, specially when its zero frequency component is comparable to its harmonics. Since the filter is applied by chunks of N samples and then all de previous data is discarded the result depends on how synchronized the input signal is respect to the accumulator present in the averaging algorithm.

This method works nice when estimating a constant value, or a really slow varying one. However, in many cases, we can get better results with a Kalman filter.

What makes this method useful is it's simplicity in both calculations and firmware implementation. All it takes is an accumulator, a count variable and a division once it's full of data.

Kalman Filter - Constant estimation

Using a Kalman filter for constant estimation may look too complicated for such a simple application, but given the measurement noise can be modeled as white and if the variance is known we can obtain very good results. Variance can also be measured, and even better: it could be calculated every certain time or at start up if the signal is known to be constant for some time. Floating point calculations might be needed, although a fixed-point approach can be used too.

There is a good example on Kalman filter constant estimation in this paper by Greg Welch and Gary Bishop.

Low Passing

The other popular and classic method is to apply a low-pass filter to the signal, preserving frequency components up to a certain value. FIR and IIR filters come to mind but I won't discuss this here since it's a whole topic on itself. Using fixed point with these filters is quite easy and straightforward as long as we know we've chosen the right resolution for the fixed point calculations. Otherwise stability problems can arise, specially with IIR ones.

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