Update README.md

README.md

@@ -1,3 +1,49 @@
 ---
 license: mit
+tags:
+- robotics
+- autonomous-vehicles
+- object-tracking
+- kalman-filter
+- fmcw-lidar
+- doppler-lidar
+- pytorch
+datasets:
+- AevaScenes
+metrics:
+- prediction-error
+pipeline_tag: motion-prediction
 ---
+
+# D-KalmanNet: Neural Kalman Filtering for Doppler LiDAR Tracking
+
+This repository contains the pre-trained weights for **D-KalmanNet**, the tracking component of the DPNet framework. D-KalmanNet integrates a structured Gaussian State Space (GSS) model with a recurrent neural network to accurately predict and track the future states of dynamic obstacles using measurements from (FMCW) **Doppler LiDAR**.
+
+The full framework can be found in the official GitHub repository.
+
+## Model Details
+
+- **Developed by:** [UUwei-zuo](https://github.com/UUwei-zuo)
+- **Dataset Trained On:** [AevaScenes](https://github.com/aevainc/aevascenes) 
+- **Framework:** PyTorch
+- **Associated Code:** [GitHub: UUwei-zuo/DPNet](https://github.com/UUwei-zuo/DPNet)
+- **Paper:** [RA-L '26][DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments](https://arxiv.org/abs/2512.00375)
+
+## How to Use
+
+Intructions for loading the pretraining `model.pt` or training your custom model can be found in [GitHub: UUwei-zuo/DPNet](https://github.com/UUwei-zuo/DPNet).
+
+## Citation
+
+```bibtex
+@article{zuo2026dpnet,
+  author={Zuo, Wei and Ren, Zeyi and Li, Chengyang and Wang, Yikun and Zhao, Mingle and Wang, Shuai and Sui, Wei and Gao, Fei and Wu, Yik-Chung and Xu, Chengzhong},
+  journal={IEEE Robotics and Automation Letters}, 
+  title={DPNet: Doppler LiDAR Motion Planning for Highly-Dynamic Environments}, 
+  year={2026},
+  volume={11},
+  number={6},
+  pages={7190-7197},
+  doi={10.1109/LRA.2026.3685933}
+}
+```