AutoKalman Library

The AutoKalman library provides an easy-to-use interface for implementing Kalman filters in Arduino projects. It simplifies the process of setting up and tuning a Kalman filter, making it ideal for applications involving sensor fusion, signal smoothing, and state estimation.

Features

• Ease of Use: Simplifies the setup of a Kalman filter with default parameters.
• Configurable: Allows dynamic adjustment of process noise, measurement noise, and error covariance.
• State Estimation: Accurately predicts the system state based on noisy measurements.
• Reset Capability: Enables reinitialization of the filter for dynamic systems.
• Getter Methods: Provides access to current filter parameters and state estimates.

Installation

1. Download the library as a ZIP file
2. In the Arduino IDE, go to Sketch > Include Library > Add .ZIP Library
3. Select the downloaded ZIP file
4. Restart the Arduino IDE

API Reference

Constructor

AutoKalman(float processNoise = 1.0, float measurementNoise = 1.0, float estimatedError = 1.0, float initialValue = 0.0);

• processNoise: The process noise covariance (Q).
• measurementNoise: The measurement noise covariance (R).
• estimatedError: The initial estimate of error covariance (P).
• initialValue: The initial state estimate.

Methods

void setProcessNoise(float q)

Sets the process noise covariance.

void setMeasurementNoise(float r)

Sets the measurement noise covariance.

void setEstimatedError(float p)

Sets the error covariance.

void setInitialValue(float value)

Sets the initial value of the state estimate.

float filter(float measurement)

Applies the Kalman filter to a new measurement and returns the updated state estimate.

void reset()

Reinitializes the filter, clearing its state.

float getProcessNoise() const

Returns the current process noise covariance.

float getMeasurementNoise() const

Returns the current measurement noise covariance.

float getEstimatedError() const

Returns the current error covariance.

float getStateEstimate() const

Returns the current state estimate.

Applications

The AutoKalman library can be used in a variety of projects, including but not limited to:

• Signal Smoothing: Reducing noise in sensor readings (e.g., temperature, pressure, or distance sensors).
• Sensor Fusion: Combining data from multiple sensors for a more accurate system state (e.g., combining gyroscope and accelerometer data).
• Robotics: Estimating the position or velocity of a robot in noisy environments.
• Navigation: Tracking objects or vehicles with noisy GPS or IMU data.

Examples

This library includes simple, intermediate, and advanced examples to help you get started. The examples cover:

• Basic signal smoothing.
• Dynamic noise adjustment.
• Multi-sensor data fusion.

Refer to the examples directory in the library for more details.

License

This library is open-source and distributed under the MIT License. You are free to use, modify, and distribute it in your projects.

Contributions

Contributions are welcome! If you encounter any issues or have suggestions for improvement, feel free to submit a pull request or open an issue in the repository.

---

With AutoKalman, integrating Kalman filters into your Arduino projects has never been easier. Start building smarter, more reliable systems today!