Exponentially Weighted Moving Average filter is used for smoothing data series readings.

Unlike the method with a history buffer that calculates an average of the last N readings, this method consumes significantly less memory and works faster.

output = alpha * reading + (1 - alpha) * lastOutput

• alpha = Smoothing factor, in range [0,1]. Higher the value - less smoothing (higher the latest reading impact)
• reading = current input value
• lastOutput = last filter output value
• output = filter output value after the last reading

Exponentially Weighted Moving Average filter template that allows restriction to a specific data type (generally a non-floating-point data type), such as uint32_t. Avoiding floating point arithmetics can significantly decrease code footprint, especially in embeded devices, such as Arduino or STM32.

Note that the integer data type has to be as big as possible (32 bits at least, signed or unsinged), in order to allow internal calculations to be correctly performed, since the result will overflow in case when a too small data type is chosen.

The filtered value in this class is calculated as:

output = (alpha * reading + (alphaScale - alpha) * lastOutput) / alphaScale

If you want to create a filter for integers, with an alpha value of 0.03, you will just do the following:

EwmaT <int> filter(3, 100)

• alpha = Smoothing factor in range [0,alphaScale]. Higher the value - less smoothing (higher the latest reading impact)
• alphaScale = Number that will divide the alpha value, in order to get the actual alpha parameter of the filter (usually 10, 100, 1000 etc.)
• lastOutput = last filter output value
• output = filter output value after the last reading