AI-Aided Kalman Filters

This repository contains the implementation and comparison of AI-aided Kalman filter techniques applied to measurements observed from a Lorenz Attractor system. The purpose of this project is to explore how artificial intelligence can enhance traditional Kalman filter methods, improving accuracy and robustness in nonlinear state estimation.

Algorithms Included

The following filters and techniques are implemented in this repository:

• Extended Kalman Filter (EKF)
• Particle Filter (PF)
• KalmanNet: an interpretable, low complexity, and data-efficient DNN-aided real-time state estimator by learning the Kalman gain.
• Data-driven Nonlinear State Estimation (DANSE): a data-driven nonlinear state estimation method.
• Augmented Physics-based Model (APBM): a model that combines physical modeling with data-driven methods for enhanced state estimation.

Project Structure

The repository is organized as follows:

• dataset/: the public dataset of the observations and ground truth.
• figs/: simulation and experiment results.
• RTSNet_IL/, DANSE_KTH, APBM_NU_CZ: the codes of implementation for the algorithm indicated by the folder name.

Getting Started

To get started with this project, clone the repository and follow the instructions in each subfolder. Please pay attention to the environment and package requirements.

Contributions

This work is a collaborative effort by researchers from:

• Ben-Gurion University, Israel
• ETH Zürich, Switzerland
• KTH Royal Institute of Technology, Sweden
• University of West Bohemia, Czech Republic
• Northeastern University, USA

Citation

For more details or to cite this work, please refer to the paper:
TBD

License

TBD.

Contact

TBD.