Yildiz Navigation Extended Kalman Filter (EKF) for IMU Sensors

Overview

This repository contains MATLAB code implementing an Extended Kalman Filter (EKF) for processing Inertial Measurement Unit (IMU) data. The EKF algorithm is used to estimate the orientation of a sensor by fusing data from accelerometers, gyroscopes, and magnetometers. This project was developed at Yildiz Technical University as part of YildizNAV Project.

Features

• IMU Data Fusion: Combines accelerometer, gyroscope, and magnetometer data to estimate orientation.
• Quaternion Representation: Uses quaternions to represent orientation internally, avoiding gimbal lock and providing smooth rotation.
• Real-time Visualization: Visualizes orientation estimates in real-time using MATLAB plots and animations.
• Modular Code Structure: Code is organized into separate functions for the EKF, Jacobian calculations, and data visualization.

Files and Directory Structure

• main.m: Main script to initialize data, run the EKF, and visualize results.
• EKF.m: Function implementing the Extended Kalman Filter.
• Fjacob.m: Function to compute the Jacobian of the state transition matrix.
• Hjacob.m: Function to compute the Jacobian of the measurement matrix.
• animateIMUState.m: Function to animate the IMU state in a 3D plot.
• plotIMUData.m: Function to plot IMU data and EKF results.
• gyrotest.txt: Example IMU data file used for testing.

Getting Started

Prerequisites

• MATLAB (any recent version should work)
• Basic understanding of IMU sensors and Kalman filters

Running the Code

1. Clone the repository to your local machine:

   git clone https://github.com/hanegeyavuz/Extended-Kalman-Filter.git
   cd Yildiz-Navigation-EKF-IMU

2. Open MATLAB and navigate to the project directory.
3. Run the main.m script:

Description

• This project processes IMU data to estimate the orientation of a device using an Extended Kalman Filter. The data is read from a text file, normalized, and then used to compute orientation in the form of Euler angles, which are subsequently visualized using MATLAB plots.

Contributing

• Contributions are welcome! If you have any suggestions or find any bugs, please open an Issue or submit a Pull Request.

License

This project is licensed under the GPL License - see the LICENSE file for details.

Authors

• Emre Emir Fidan
• Muhammed Yavuz Hanege
• Mehmet Emre Eyvaz
• Kerem Vatansever

Acknowledgements

• Yildiz Technical University for supporting this project.
• All contributors and users for their valuable feedback and suggestions.