UAV-State-Estimation

Overview

Welcome to UAV-State-Estimation, a comprehensive Python repository for 3D state estimation of unmanned aerial vehicles (UAVs) using Kalman Filter techniques. This repository offers a unique blend of theoretical models and practical simulations, perfect for enthusiasts and professionals in the field of drone technology and autonomous systems.

Features

• 3D Kalman Filter Tracking: Utilizing the KalmanFilterModel class from kftracker3d.py, the repository implements a robust Kalman Filter for accurate state estimation in three dimensions.
• Drone Dynamics Model: The DroneModel3D class in drone_model_3d.py simulates the physical behavior of a UAV, incorporating key dynamics like position, velocity, and orientation.
• Interactive Simulations: drone_tracker3d.py provides tools for running simulations and visualizing the results in a dynamic and informative manner.
• Extensible Framework: The base class KalmanFilterBase in kfmodels.py lays the foundation for future extensions and customizations of the Kalman Filter.
• Comprehensive Simulation Control: kalman_main.py serves as the entry point for the simulation, offering various parameters for customizing the UAV's motion and sensor characteristics.

Getting Started

1. Installation: Clone the repository and ensure you have all required dependencies, such as NumPy, pandas, and Matplotlib, installed.
2. Running a Simulation: Execute kalman_main.py to start a simulation. You can modify the simulation options within this script to suit your experimental needs.
3. Visualization: Observe the performance of the Kalman Filter in real-time through the plots generated during the simulation.

Usage Examples

• State Estimation in Noisy Environments: Demonstrate how the Kalman Filter maintains accurate state estimates despite measurement noise.
• Motion Pattern Analysis: Explore how different UAV motion patterns affect the performance of the state estimator.
• Sensor Fusion: Extend the framework to incorporate multiple sensors for more robust state estimation.

Contribution

Contributions are welcome! Whether it's extending the models, improving the simulations, or fixing bugs, your input is valuable.

Acknowledgments

Special thanks to all contributors and users of this repository for your support and feedback.