Belief-Propagation based Target Handover in Distributed Integrated Sensing and Communication

This repository contains the implementation and experimental data for the paper "Belief-Propagation based Target Handover in Distributed Integrated Sensing and Communication" published at GlobeCom 2025. The code is adapted from https://github.com/meyer-ucsd/MTT-PIEEE-18.

📖 Abstract

This work presents a belief-propagation-based approach for target handover in distributed integrated sensing and communication (ISAC) systems. Our method enables efficient coordination between multiple base stations for seamless target tracking and handover decisions.

🚀 Features

• Centralized Tracking: Traditional centralized target tracking implementation
• Distributed Tracking: Distributed target tracking using belief propagation
• Target Handover: Intelligent handover mechanism between base stations
• Performance Evaluation: GOSPA metrics and comprehensive evaluation tools (this is like a Chalmers specific thing, not really necessary. You don't have to spend time understand it, and I won't use this metrics going forward.)
• Visualization: Rich visualization tools for simulation results and tracking performance

📁 Project Structure

BPTargetHandover/
├── TrackerBP*.py          # Core tracking algorithms
├── centralized.py         # Centralized tracking simulation
├── distributed.py         # Distributed tracking simulation  
├── handover.py            # Handover simulation
├── Utils.py               # Utility functions
├── GOSPA.py              # GOSPA evaluation metrics
├── evaluate.py           # Performance evaluation
├── generate_data.py      # Data generation scripts
├── visualize_*.py        # Visualization scripts
├── BS*_subplots/         # Base station subplot data
└── Visualization/        # Generated visualization outputs

🛠️ Installation

1. Clone the repository:

git clone https://github.com/yourusername/BPTargetHandover.git
cd BPTargetHandover

2. Install required dependencies:

pip install numpy matplotlib scipy pandas

🎯 Usage

Running Simulations

Generating Data

This is to generate the data of 100 Experiments. For centralized, distributed and handover, the same dataset will be used for comparison.

python generate_data.py

Centralized Tracking

python centralized.py

Distributed Tracking

python distributed.py

Target Handover

python handover.py

Evaluation

python evaluate.py

Visualization

python visualize_simulation_scenario.py  # Simulation environment
python visualize_centralized_result.py   # Centralized results
python visualize_distributed_result.py   # Distributed results
python visualize_handover_result.py      # Handover results

📊 Experimental Data

The complete experimental dataset is available on Google Drive: Download Experimental Data

🎥 Demonstrations

Simulation Environment

Tracking Results (Particle-based)

Centralized Tracking	Distributed Tracking	Target Handover

EKF Covariance Visualizations

Below are EKF-based tracking visualizations showing per-track covariance ellipses (2-sigma) instead of particle clouds.

Centralized EKF	Distributed EKF	Handover EKF

📈 Performance Metrics

The project evaluates performance using:

• GOSPA (Generalized Optimal Sub-Pattern Assignment) metrics
• Localization accuracy measurements
• False track analysis
• Switching cost evaluation
• Target handover efficiency metrics

📝 Citation

If you use this code in your research, please cite our paper:

@INPROCEEDINGS{11432274,
  author={Bai, Liping and Ge, Yu and Wymeersch, Henk},
  booktitle={GLOBECOM 2025 - 2025 IEEE Global Communications Conference}, 
  title={Belief Propagation-based Target Handover in Distributed Integrated Sensing and Communication}, 
  year={2025},
  volume={},
  number={},
  pages={752-757},
  keywords={6G mobile communication;Base stations;Target tracking;Computational modeling;Scalability;Urban areas;Handover;Integrated sensing and communication;Trajectory;Belief propagation;6G;DISAC;tracking;trajectory;target handover;belief propagation},
  doi={10.1109/GLOBECOM59602.2025.11432274}}
}

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📄 License

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

📧 Contact

For questions or collaboration opportunities, please contact:

• Primary Author: [your.email@domain.com]
• Project Homepage: [https://github.com/yourusername/BPTargetHandover]

🙏 Acknowledgments

This work was supported by [funding source] and developed at [institution name]. We thank the reviewers and colleagues for their valuable feedback.