Median Filter

A graph with two peaks with the input closely following the target signal.

A statistically robust alternative to the moving-average filter is the median filter. Where a moving average filter takes the arithmetic mean of the input over a moving sample window, a median filter (per the name) takes a median instead.

The median filter is most-useful for removing occasional outliers from an input stream. This makes it particularly well-suited to filtering inputs from distance sensors, which are prone to occasional interference. Unlike a moving average, the median filter will remain completely unaffected by small numbers of outliers, no matter how extreme.

The median filter is supported in WPILib through the MedianFilter class (Java, C++, , Python).

Creating a MedianFilter

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The C++ MedianFilter class is templated on the data type used for the input.

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Because filters have ”memory“, each input stream requires its own filter object. Do not attempt to use the same filter object for multiple input streams.

Creating a MedianFilter is simple:

// Creates a MedianFilter with a window size of 5 samples
MedianFilter filter = new MedianFilter(5);
// Creates a MedianFilter with a window size of 5 samples
frc::MedianFilter<double> filter(5);
from wpimath.filter import MedianFilter
# Creates a MedianFilter with a window size of 5 samples
filter = MedianFilter(5)

Using a MedianFilter

Once your filter has been created, using it is easy - simply call the calculate() method with the most recent input to obtain the filtered output:

// Calculates the next value of the output
filter.calculate(input);
// Calculates the next value of the output
filter.Calculate(input);
# Calculates the next value of the output
filter.calculate(input)