Fast and memory efficient moving average calculation

Question

I'm looking for a time and memory efficient solution to calculate a moving average in C. I need to avoid dividing because I'm on a PIC 16 which has no dedicated division unit.

At the moment, I just store all values in a ring buffer and simply store and update the sum each time a new value arrives. This is really efficient, but unfortunately uses most of my available memory...

Answer 1

As others have mentioned, you should consider a IIR (infinite impulse response) filter rather than the FIR (finite impulse response) filter you are using now. There is more to it, but at first glance FIR filters are implemented as explicit convolutions and IIR filters with equations.

The particular IIR filter I use a lot in microcontrollers is a single pole low pass filter. This is the digital equivalent of a simple R-C analog filter. For most applications, these will have better characteristics than the box filter that you are using. Most uses of a box filter that I have encountered are a result of someone not paying attention in digital signal processing class, not as a result of needing their particular characteristics. If you just want to attenuate high frequencies that you know are noise, a single pole low pass filter is better. The best way to implement one digitally in a microcontroller is usually:

FILT <-- FILT + FF(NEW - FILT)

FILT is a piece of persistant state. This is the only persistant variable you need to compute this filter. NEW is the new value that the filter is being updated with this iteration. FF is the filter fraction, which adjusts the "heaviness" of the filter. Look at this algorithm and see that for FF = 0 the filter is infinitely heavy since the output never changes. For FF = 1, it's really no filter at all since the output just follows the input. Useful values are in between. On small systems you pick FF to be 1/2^N so that the multiply by FF can be accomplished as a right shift by N bits. For example, FF might be 1/16 and the multiply by FF therefore a right shift of 4 bits. Otherwise this filter needs only one subtract and one add, although the numbers usually need to be wider than the input value.

I usually take A/D readings significantly faster than they are needed and apply two of these filters cascaded. This is the digital equivalent of two R-C filters in series, and attenuates by 12 dB/octave above the rolloff frequency. However, for A/D readings it's usually more relevant to look at the filter in the time domain by considering its step response. This tells you how fast your system will see a change when the thing you are measuring changes.

To facilitate designing these filters (which only means picking FF and deciding how many of them to cascade), I use my program FILTBITS. You specify the number of shift bits for each FF in the cascaded series of filters and it computes the step response and other values. Actually I usually run this via my wrapper script PLOTFILT. This run FILTBITS, which makes a CSV file, then plots the CSV file. For example, here is the result of "PLOTFILT 4 4":

The two parameters to PLOTFILT mean there will be two filters cascaded of the type described above. The values of 4 indicate the number of shift bits to realize the multiply by FF. The two FF values are therefore 1/16 in this case. The red trace is the unit step response, and is the main thing to look at. For example, this tells you that if the input changes instantaneously, the output of the combined filter will settle to 90% of the new value in 60 iterations. If you care about 95% settling time then you have to wait about 73 iterations, and for 50% settling time only 26 iterations. The green trace shows you the output from a single full amplitude spike. This gives you some idea of the random noise suppression. It looks like no single sample will cause more than a 2.5% change in the output. The blue trace is to give a subjective feeling of what this filter does with white noise. This is not a rigorous test since there is no guarantee what exactly the content was of the random numbers picked as the white noise input for this run of PLOTFILT. It's only to give you a rough feeling of how much it will be squashed and how smooth it is.

PLOTFILT, maybe FILTBITS, and lots of other useful stuff, especially for PIC firmware development is available in the PIC Development Tools software release at my Software downloads page.

Answer 2

If you can live with the restriction of a power of two number of items to average (ie 2,4,8,16,32 etc) then the divide can easily and efficiently be done on a low performance micro with no dedicated divide because it can be done as a bit shift. Each shift right is one power of two eg:

avg = sum >> 2; //divide by 2^2 (4)

or

avg = sum >> 3; //divide by 2^3 (8)

etc.

Answer 3

There is an answer for a true moving average filter (aka "boxcar filter") with less memory requirements, if you don't mind downsampling. It's called a cascaded integrator-comb filter (CIC). The idea is that you have an integrator which you take differences of over a time period, and the key memory-saving device is that by downsampling, you don't have to store every value of the integrator. It can be implemented using the following pseudocode:

function out = filterInput(in)
{
   const int decimationFactor = /* 2 or 4 or 8 or whatever */;
   const int statesize = /* whatever */
   static int integrator = 0;
   static int downsample_count = 0;
   static int ringbuffer[statesize];
   // don't forget to initialize the ringbuffer somehow
   static int ringbuffer_ptr = 0;
   static int outstate = 0;

   integrator += in;
   if (++downsample_count >= decimationFactor)
   {
     int oldintegrator = ringbuffer[ringbuffer_ptr];
     ringbuffer[ringbuffer_ptr] = integrator;
     ringbuffer_ptr = (ringbuffer_ptr + 1) % statesize;
     outstate = (integrator - oldintegrator) / (statesize * decimationFactor);
   }
   return outstate;
}

Your effective moving average length is decimationFactor*statesize but you only need to keep around statesize samples. Obviously you can get better performance if your statesize and decimationFactor are powers of 2, so that the division and remainder operators get replaced by shifts and mask-ands.

---

Postscript: I do agree with Olin that you should always consider simple IIR filters before a moving average filter. If you don't need the frequency-nulls of a boxcar filter, a 1-pole or 2-pole low-pass filter will probably work fine.

On the other hand, if you are filtering for the purposes of decimation (taking a high-sample-rate input and averaging it for use by a low-rate process) then a CIC filter may be just what you're looking for. (especially if you can use statesize=1 and avoid the ringbuffer altogether with just a single previous integrator value)

Answer 4

You can approximate a moving avarage for some applications with a simple IIR filter.

weight is 0..255 value, high values = shorter timescale for avaraging

Value = (newvalue*weight+value*(256-weight))/256

To avoid rounding errors, value would normally be a long, of which you only use higher -order bytes as your 'actual' value.

Answer 5

As mikeselectricstuff said, if you really need to reduce your memory needs, and you don't mind your impulse response being an exponential (instead of a rectangular pulse), I would go for an exponential moving average filter. I use them extensively. With that type of filter, you don't need any buffer. You don't have to store N past samples. Just one. So, your memory requirements get cut down by a factor of N.

Also, you don't need any division for that. Only multiplications. If you have access to floating-point arithmetic, use floating-point multiplications. Otherwise, do integer multiplications and shifts to the right. However, we are in 2012, and I would recommend you to use compilers (and MCUs) that allow you to work with floating-point numbers.

Besides being more memory efficient and faster (you don't have to update items in any circular buffer), I would say it is also more natural, because an exponential impulse response matches better the way nature behaves, in most cases.

Answer 6

Here's a single-pole low-pass filter (moving average, with cutoff frequency = CutoffFrequency). Very simple, very fast, works great, and almost no memory overhead.

Note: All variables have scope beyond the filter function, except the passed in newInput

// One-time calculations (can be pre-calculated at compile-time and loaded with constants)
DecayFactor = exp(-2.0 * PI * CutoffFrequency / SampleRate);
AmplitudeFactor = (1.0 - DecayFactor);

// Filter Loop Function ----- THIS IS IT -----
double Filter(double newInput)
{
   MovingAverage *= DecayFactor;
   MovingAverage += AmplitudeFactor * newInput;

   return (MovingAverage);
}

Note: This is a single stage filter. Multiple stages can be cascaded together to increase the sharpness of the filter. If you use more than one stage, you'll have to adjust DecayFactor (as relates to the Cutoff-Frequency) to compensate.

And obviously all you need is those two lines placed anywhere, they don't need their own function. This filter does have a ramp-up time before the moving average represents that of the input signal. If you need to bypass that ramp-up time, you can just initialize MovingAverage to the first value of newInput instead of 0, and hope the first newInput isn't an outlier.

(CutoffFrequency/SampleRate) has a range of between 0 and 0.5. DecayFactor is a value between 0 and 1, usually close to 1.

Single-precision floats are good enough for most things, I just prefer doubles. If you need to stick with integers, you can convert DecayFactor and Amplitude Factor into fractional integers, in which the numerator is stored as the integer, and the denominator is an integer power of 2 (so you can bit-shift to the right as the denominator rather than having to divide during the filter loop). For example, if DecayFactor = 0.99, and you want to use integers, you can set DecayFactor = 0.99 * 65536 = 64881. And then anytime you multiply by DecayFactor in your filter loop, just shift the result >> 16.

For more information on this, an excellent book that's online, chapter 19 on recursive filters: http://www.dspguide.com/ch19.htm

P.S. For the Moving Average paradigm, a different approach to setting DecayFactor and AmplitudeFactor that may be more relevant to your needs, let's say you want the previous, about 6 items averaged together, doing it discretely, you'd add 6 items and divide by 6, so you can set the AmplitudeFactor to 1/6, and DecayFactor to (1.0 - AmplitudeFactor).

Answer 7

There's some in-depth analysis of the math behind using the first order IIR filter that Olin Lathrop has already described over on the Digital Signal Processing stack exchange (includes lots of pretty pictures.) The equation for this IIR filter is:

y[n]=αx[n]+(1−α)y[n−1]

This can be implemented using only integers and no division using the following code (might need some debugging as I was typing from memory.)

/**
*  @details    Implement a first order IIR filter to approximate a K sample 
*              moving average.  This function implements the equation:
*
*                  y[n] = alpha * x[n] + (1 - alpha) * y[n-1]
*
*  @param      *filter - a Signed 15.16 fixed-point value.
*  @param      sample - the 16-bit value of the current sample.
*/

#define BITS 2      ///< This is roughly = log2( 1 / alpha )

short IIR_Filter(long *filter, short sample)
{
    long local_sample = sample << 16;

    *filter += (local_sample - *filter) >> BITS;

    return (short)((*filter+0x8000) >> 16);     ///< Round by adding .5 and truncating.
}

This filter approximates a moving average of the last K samples by setting the value of alpha to 1/K. Do this in the preceding code by #defineing BITS to LOG2(K), i.e. for K = 16 set BITS to 4, for K = 4 set BITS to 2, etc.

(I'll verify the code listed here as soon as I get a change and edit this answer if needed.)