A lightweight, efficient Arduino library that implements various software-based analog filters aimed for real-time signal processing. Large-scale projects usually use multimodal sensor data, each of which may require different filters. This library was born from a genuine requirement at my research job. Perfect for quick experimentation between different filters for smoothing sensor data, removing noise, and signal conditioning without additional hardware.
- Multiple Analog Sensors at a go: Mention direct GPIO pin number. Add as many analog sensors as you want.
- Multiple Filter Types: Rolling Moving Average, Rolling Median, Exponential IIR, and 2nd Order IIR Butterworth filters
- Configurable Parameters: Easy setup through configuration structures
- Memory Efficient: Dynamic allocation only for required, active filters
- Real-time Filtering: Aimed for embedded systems
- Arduino Compatible: Works with any Arduino-IDE compatible board with analog pins
MultiSensorFilter/
├── examples/
├── results/
├── src/
├ Changelog
├ LICENSE
├ library.properties
├ keywords
└ README
| Filter Type | Description | Parameters |
|---|---|---|
MOV_AVG | Moving Average | param1: Window size (2-50 samples), param2: NA |
MEDIAN | Median Filter | param1: Window size (2-50 samples), param2: NA |
EXPONENTIAL | Exponential IIR Smoothing | param1: Alpha (0-1, lower = more smoothing, slower response), param2: NA |
BUTTER2_LPF | 2nd Order Butterworth Low Pass | param1: Cutoff freq (Hz), param2: Q factor |
BUTTER2_HPF | 2nd Order Butterworth High Pass | param1: Cutoff freq (Hz), param2: Q factor |
BUTTER2_BPF | 2nd Order Butterworth Band Pass | param1: Center freq (Hz), param2: Q factor |
BUTTER2_NOTCH | 2nd Order Butterworth Notch | param1: Notch freq (Hz), param2: Q factor |
LINEAR_KALMAN | Simple Kalman Filter | param1: Q (process noise variance), param2: R (measurement noise variance) |
- Open Arduino IDE
- Go to Sketch → Include Library → Manage Libraries
- Search for "SoftwareAnalogFilters"
- Click Install
- Download the latest release from GitHub
- Extract to your Arduino libraries folder
- Restart Arduino IDE
#include"MultiSensorFilter.h"
MultiSensorFilter filt;
// Define your filter configuration
FilterConfig Config[] = {
{GPIO0, "Sensor1", MOV_AVG, 10}, // Moving average, 10 samples
{GPIO1, "Sensor2", BUTTER2_LPF, 5, 0.707}, // Low pass, 5Hz cutoff
{GPIO3, "Sensor3", EXPONENTIAL, 0.2} // Exponential smoothing
};
constsize_t config_len = sizeof(Config) / sizeof(Config[0]);
voidsetup() {
Serial.begin(9600);
filt.Init(Config, config_len, 100); // 100Hz system acquisition rate
}
voidloop() {
float filtered_value = filt.analog_filter(GPIO0);
Serial.println(filtered_value);
delay(10);
}
Refer results folder
Q: Filter not working on my pin A: Ensure the pin is configured in your FilterConfig array and properly initialized.
Q: Butterworth filter behaving strangely A: Check that your acquisition rate (acq_hz) is correctly set and at least 2× your filter frequency.
Q: Memory allocation errors A: Reduce filter window sizes or number of active filters to conserve memory.
- Currently assumes all input GPIOs need a Filter. That may not be the case.
- Needs to write better find_filters_idx code. Currently runs a loop for each GPIO, not ideal. Have thought of introducing a context / filter entry pointer.
- Add more filters.
- The .ino interface needs some refactoring. Automatic calculation of the size of FilterConfig array needs to be done at the backend.
- Only works for ADC reads. Should work for digital sensor data like I2C, SPI, UART also.
- https://webaudio.github.io/Audio-EQ-Cookbook/audio-eq-cookbook.html
- https://e2e.ti.com/support/audio-group/audio/f/audio-forum/911062/ccs-tas5825m-we-need-software-source-code-of-the-ppc3-eq-module
- N. IWANAGA, T. MATSUMURA, A. YOSHIDA, W. KOBAYASHI, and T. ONOYE, “Embedded System Implementation of Sound Localization in Proximal Region,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, doi: https://doi.org/10.1093/ietfec/e91-a.3.763.
- https://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf