Filters Component

The filters component contains various types of filters that can be used for various signal processing and state estimation tasks.

Table of Contents

• Filters Component
  • Biquad Filter
  • Butterworth Filter
  • Complementary Filter
  • Kalman Filter
  • Lowpass Filter
  • Madgiwck Filter
  • Simple Lowpass Filter
  • SoS (Second-Order Sections) Filter
  • Transfer Function
  • Example

Biquad Filter

The BiquadFilter class provides an implementation of a Digital Biequad Filter with implementations for both the Direct Form 1 and Direct Form 2.

Butterworth Filter

The ButterworthFilter class provides an implementation of a Digital Butterworth Filter, implemented as biquad sections (second order sections).

Complementary Filter

The ComplementaryFilter provides a simple implementation of a filter specifically designed to combine the outputs of a gyroscope and an accelerometer to produce a more accurate estimate of the orientation of an object. This filter is provided for completeness and simplicity, but the KalmanFilter or MadgwickFilter are generally preferred for this purpose.

Kalman Filter

The KalmanFilter class implements a Kalman filter for linear systems. The filter can be used to estimate the state of a linear system given noisy measurements. The filter can be configured for an arbitrary number of states and measurements. You can also specify the process noise and measurement noise covariances.

To use the filter, you must first create an instance of the KalmanFilter class, then call the predict and update methods to estimate the state of the system. To get the current state estimate, call the get_state method.

Lowpass Filter

The LowpassFilter class provides an implementation of a digial lowpass infinite impulse response (IIR) filter, which leverages the hardware acceleration provided by esp-dsp and which leverages the vector instructions on the espressif processors.

Madgiwck Filter

The MadgwickFilter implements the Madgwick algorithm for orientation estimation using an IMU. The algorithm is based on the paper An efficient orientation filter for inertial and inertial/magnetic sensor arrays_ by Sebastian Madgwick. It supports state / orientation estimation for both 6-axis IMUs as well as for 9-axis IMUs.

Simple Lowpass Filter

The SimpleLowpassFilter class provides an implementation of a simple moving average filter with a configurable time constant.

SoS (Second-Order Sections) Filter

The SosFilter class provides an implementation of a Second Order Sections (SoS) filter. For more information please see series second-order sections as well as Digital Biquad Filter.

Transfer Function

The TransferFunction struct provides a simple container for storing the A and B coefficients for an N-th order transfer function.

Example

This example shows how to use a LowpassFilter, ButterworthFilter, and SimpleLowpassFilter from the filters component to filter signals. This example simply operates on random perturbations of auto-generated data.