SoftFilters

Arduino framework and library of software data filters. Can be used as a generic filter framework in C++.

Quick Start

0. Installation

   The SoftFilters library can be conveniently installed through the Arduino IDE's library manager.

   Alternatively, you can manually clone from this Github repository to your Arduino library directory:

   $ cd ~/Arduino/libraries/  # enter Arduino library directory
   $ git clone https://github.com/haimoz/SoftFilters.git

1. Include the library

   #include <SoftFilters.h>

2. Create a filter

   This can be done in the global scope of your Arduino sketch. You will need to specify the data type for most filters, which provides more flexibility to your sketch. This is a C++ feature called "templates" which is also supported in Arduino.

   MovingAverageFilter<double> movAvg(20);  // a 20-sample moving average filter

3. Push the data through the filter

   Suppose your input data is in the variable input_val. And don't forget to declare an output variable to hold the output data. You can declare the input & output variables in the global scope of your Arduino sketch:

   double input_val, output_val;

   Call the filter's push method, and pass the input and output data by pointer. The push method will return true if there is valid output value.

   void loop()
   {
     ...  // suppose you have read data to the input_val variable
     if (movAvg.push(&input_val, &output_val)) {
       ...  // now you can process the output_val variable
     }
   }

4. Speed and acceleration

   Two types are provided for the calculation of speed and acceleration of your data: Reading and Differential. Reading is a <value, timestamp> tuple. Differential is a three-value tuple of position, speed, and acceleration. Using these types, you can create a DifferentialFilter to calculate the speed and acceleration.

   DifferentialFilter<double, usigned long> diff;
   
   // need to specify the value and timestamp data type
   Reading<double, unsigned long> r;
   
   // output of a differential filter is a `Reading` with the value type of `Differential`
   Reading<Differential<double>, unsigned long> d;
   
   void setup()
   {
     ...
   }
   void loop()
   {
     ...  // get the input into the reading
     if (diff.push(&r, &d)) {
       Serial.print(r.value);              Serial.print(" ");
       Serial.print(r.timestamp);          Serial.print(" ");
       Serial.print(d.value.position);     Serial.print(" ");
       Serial.print(d.value.speed);        Serial.print(" ");
       Serial.print(d.value.acceleration);
     }
     Serial.println();
     ...  // further process the output in the differential
   }

5. Create a filter chain

   Create a FilterChain to connect multiple filters. Then you can use it as a single filter. When appending the sub-filters, you need to pass by pointer.

   double input;
   FilterChain filters;
   MovingAverageFilter<double movAvg(20);
   DifferentialFilter<double, unsigned long> diff;
   Reading<Differential<double>, unsigned long> output;
   
   void setup()
   {
     ...
     filters.append(&movAvg);
     filters.append(&diff);
     ...
   }
   
   void loop()
   {
     ...  // get input
     if (filters.push(&input, &output)) {
       Serial.print(d.value.position);     Serial.print(" ");
       Serial.print(d.value.speed);        Serial.print(" ");
       Serial.print(d.value.acceleration); Serial.print(" ");
       Serial.print(d.timestamp);
       ...  // further process output
     }
     ...
   }

Currently implemented filters

• DifferentialFilter

  Calculates the speed and acceleration.

• TimestampFilter

  Adds timestamp to input data, outputting the Reading data type.

• MovingAverageFilter

  Calculates the average of samples in a moving window.

• MovingVarianceFilter

  Calculates the variance of samples in a moving window.

• WeightedUpdateFilter

  Updates the output value based on a weighted average between the previous output and the current input.

• AdaptiveNormalizationFilter

  Outputs a value between 0 and 1 which is the current input normalized against the range of all input (including the current input).

• OneEuroFilter

  An adaptive low-pass filter based on the following paper:

  Géry Casiez, Nicolas Roussel, and Daniel Vogel. 2012. 1€ Filter: A Simple Speed-based Low-pass Filter for Noisy Input in Interactive Systems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '12), 2527–2530. https://doi.org/10.1145/2207676.2208639

• LambdaFilter

  A filter that uses a user-supplied filter function.

• FlowRateFilter

  A pass-through filter for measuring the data rate.

Documentations

For more details, see documentations or the source code.

Arduino examples

See examples directory.